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Accurate evaluation of electrophysiological and morphological characteristics of the skeletal muscles is critical to establish a comprehensive assessment of the human neuromusculoskeletal function in vivo. However, current technological challenges lie in unsynchronized and unparallel operation of separate acquisition systems such as surface electromyography (sEMG) and ultrasonography. Key problem is the lack of ultrasound transparency of current electrophysiological electrodes. In this work, ultrasound (US) transparent electrode based on cellulose nanofibrils (CNF) substrate are proposed to solve the issue. US transparency of the electrodes are evaluated using a standard US phantom. The effects of nanocellulose type and ion-bond introduction on electrode performance is investigated. Simultaneous US image and sEMG signal acquisition of biceps brachii during isometric muscle contraction are studied. Reliable correlation analysis of the US and sEMG signals is realized which is rarely reported in the previous literatures. Recyclability and biodegradability of the current electrode are evaluated. The reported technology opens up new pathways to provide coupled anatomical and electrical information of the skeletal muscles, enables reliable anatomical and electrical information correlation analysis and largely simplify the sensor integration for assessment of the human neuromusculoskeletal function., QC 20240926

Atrial fibrillation (AF) is the most common arrhythmia in the world, leading to a significant burden to patients and the healthcare system. It is characterised by rapid and irregular atrial contractions stemming from disorganised electrical activity in the atria. The atrioventricular (AV) node regulates heart rate during AF by filtering electrical impulses from the atria.However, for persistent AF, the regulating capabilities of the AV node are often insufficient in regards to maintaining a healthy heart rate. Thus, rate control drugs affecting the conduction properties of the AV node are the most common treatment, chosen empirically for each patient. This takes time and may result in a sub-optimal drug choice. Quantifying individual differences in AV-nodal function is therefore interesting in order to potentially aid in personalised treatment selection.This thesis focuses on assessing the conduction properties of the AV node during AF from electrocardiography recordings, specifically the refractory period and conduction delay. The thesis comprises an introduction to the anatomy of the heart, AF, cardiac modelling, and parameter estimation, as well as four papers. The first paper proposes a mathematical model of the AV node where the model parameters could be estimated from 15-minute ECG recordings utilising a genetic algorithm. In the second paper, we used the proposed model and introduced a computationally efficient dynamic genetic algorithm to enable estimation of 24-hour model parameter trends, with a temporal resolution of one estimate per 1000 RR intervals, to analyse individual and drug-dependent differences in the model parameters. In the third paper, the optimisation framework was further extended to combine an Approximate Bayesian computation algorithm with the previously proposed genetic algorithm in order to quantify the uncertainty of the model parameter estimates. Additionally, a model parameter reduction step was introduced to increase interpretabilit

Smartwatches, fitness trackers, and the broader range of wearable technologies have experienced widespread adoption, resulting in a robust market characterized by an excessive quantity of diverse products. This growing collection of options may nevertheless, perplex athletes endeavoring to select the most suitable choice within this field of interest. The study aims to simplify the complex landscape of wearable technologies, focusing on smartwatches, fitness trackers, and their embedded sensors. Additionally, the study validates a few solutions that could offer an effective and cost-efficient solution for measuring cadence in athletes. The study further analyzes and categorizes widely-used commercial sports wearables, offering a matching selection of the most relevant devices for measuring key sports performance metrics. The final results presents a comparative analysis of the most accessible wearables on the market, with the aim of conducting a gap analysis that contrasts the key performance indicators (KPIs) relevant to runners with the capabilities of research-grade tools. This comparison assesses how effectively, accurately, and affordably current market products and sensors can measure athletes’ required KPIs., Smartklockor, träningsspårare och det bredare utbudet av bärbara teknologier har fått en bred användning, vilket resulterat i en robust marknad som kännetecknas av en alltför stor mängd olika produkter. Denna växande samling av alternativ kan ändå förvirra idrottare som strävar efter att välja det mest lämpliga valet inom detta intresseområde. Studien syftar till att förenkla det komplexa landskapet av bärbara teknologier, med fokus på smarta klockor, träningsspårare och deras inbyggda sensorer. Dessutom validerar studien några lösningar som kan erbjuda en effektiv och kostnadseffektiv lösning för att mäta kadens hos idrottare. Studien analyserar och kategoriserar vidare allmänt använda kommersiella sportkläder, och erbjuder ett matchande urval av de mest relevanta enheterna för att mäta viktiga sportprestandamått. De slutliga resultaten presenterar en jämförande analys av de mest tillgängliga bärbara produkterna på marknaden, med syftet att genomföra en gapanalys som kontrasterar nyckelprestandaindikatorerna (KPI:er) som är relevanta för löpare med kapaciteten hos forskningsverktyg. Den här jämförelsen bedömer hur effektivt, exakt och prisvärt aktuella marknadsprodukter och sensorer kan mäta idrottares nödvändiga nyckeltal.

Point-of-need devices perform analytical tests that help inform decisions where they are needed, away from modern lab infrastructure, be it in-field or in resource-poor settings. They have many applications, including veterinary medicine, agriculture, food safety, environmental monitoring, and forensics. In medical diagnostics, such devices are called point-of-care tests, and they could help combat societal challenges such as the spread of epidemic diseases and providing adequate healthcare in developing countries. Point-of-care devices could also be wearable to non-invasively monitor body fluids such as sweat or urine from the patient. Ideal point-of-care devices conform with the REASSURED criteria, that they should be Real-time connected, Easy to collect samples, Affordable, Sensitive, Specific, User-friendly, Rapid, robust, Equipment-free, environmentally friendly, and Deliverable to the end user. We have here developed Point-of-need devices based on textiles and Printed Circuit Boards (PCBs); both well-established technologies that could offer low-cost mass production using existing industrial resources. Specifically, we have made electrochemical biosensors based on gold-coated yarn in a rolling architecture, as well as combined with wicking Coolmax® yarn acting as microfluidic channels in wearable systems, enabling advanced textile-based diagnostic devices suitable for automation or machine-stitching into fabrics. We also showed biosensors based on gold-coated PCBs that can connect to portable potentiostats for electrochemical detection and have integrated heating for isothermal nucleic acid amplification., Fälttestsenheter utför analytiska tester som hjälper till att informera beslut där de behövs, separat från modern labbinfrastruktur, vare sig det är i fält eller i resurssvaga områden. De har många tillämpningar, inklusive veterinärmedicin, jordbruk, livsmedelssäkerhet, miljöövervakning och kriminalteknik. Inom medicinsk diagnostik kallas sådana apparater patientnära tester, och de kan hjälpa till att bekämpa samhällsutmaningar såsom spridning av epidemiska sjukdomar och tillhandahållande av adekvat sjukvård i utvecklingsländer. Patientnära testenheter kan också vara bärbara för att icke-invasivt övervaka kroppsvätskor såsom svett eller urin från patienten. Idealiska patientnära enheter korresponderar med REASSURED-kriterierna, att de ska vara realtidsanslutna, lätta att samla in prover, prisvärda, känsliga, specifika, användarvänliga, snabba, robusta, utrustningsfria, miljövänliga och leveransbara till slutanvändaren. Vi har här utvecklat fälttestenheter baserade på textilmaterial och mönsterkort; båda väletablerade teknologier som skulle kunna erbjuda massproduktion till låg kostnad med hjälp av befintliga industriella resurser. Specifikt har vi tillverkat elektrokemiska biosensorer baserade på guldbelagt garn i en rullande arkitektur, samt kombinerat med vätande Coolmax®-garn som fungerar som mikrofluidkanaler i bärbara system, vilket möjliggör avancerade textilbaserade diagnostiska enheter lämpliga för automatisering eller maskinsömnad i tyger. Vi visade också biosensorer baserade på guldbelagda PCB som kan ansluta till bärbara potentiostater för elektrokemisk detektering ha integrerad värmefunktion för isotermisk nukleinsyraamplifiering., QC 20241003

The anatomy of children’s heads is unique and distinct from adults, with smaller and softer skulls and unfused fontanels and sutures. Despite this, most current helmet testing standards for children use the same peak linear acceleration threshold as for adults. It is unclear whether this is reasonable and otherwise what thresholds should be. To answer these questions, helmet-protected head responses for different ages are needed which is however lacking today. In this study, we apply continuously scalable PIPER child head models of 1.5, 3, and 6 years old (YO), and an upgraded 18YO to study child helmet protection under extensive linear and oblique impacts. The results of this study reveal an age-dependence trend in both global kinematics and tissue response, with younger children experiencing higher levels of acceleration and velocity, as well as increased skull stress and brain strain. These findings indicate the need for better protection for younger children, suggesting that youth helmets should have a lower linear kinematic threshold, with a preliminary value of 150g for 1.5-year-old helmets. However, the results also show a different trend in rotational kinematics, indicating that the threshold of rotational velocity for a 1.5YO is similar to that for adults. The results also support the current use of small-sized adult headforms for testing child helmets before new child headforms are available., QC 20240122

Ski racing is a high-risk sport for traumatic brain injury. A better understanding of the injury mechanism and the development of effective protective equipment remains central to resolving this urgency. Finite element (FE) models are useful tools for studying biomechanical responses of the brain, especially in real-world ski accidents. However, real-world accidents are often captured by handheld monocular cameras; the videos are shaky and lack depth information, making it difficult to estimate reliable impact velocities and posture which are critical for injury prediction. Introducing novel computer vision and deep learning algorithms offers an opportunity to tackle this challenge. This study proposes a novel framework for estimating impact kinematics from handheld, shaky monocular videos of accidents to inform personalized impact simulations. The utility of this framework is demonstrated by reconstructing a ski accident, in which the extracted kinematics are input to a neuroimaging-informed, personalized FE model. The FE-derived responses are compared with imaging-identified brain injury sites of the victim. The results suggest that maximum principal strain may be a useful metric for brain injury. This study demonstrates the potential of video-informed accident reconstructions combined with personalized FE modeling to evaluate individual brain injury. Statement of significance: Reconstructing real-world sports accidents combined with finite element (FE) models presents a unique opportunity to study brain injuries, as it enables simulating complex loading conditions experienced in reality. However, a significant challenge lies in accurately obtaining kinematics from the often shaky, handheld video footage of such accidents. We propose a novel framework that bridges the gap between real-world accidents and video-informed injury predictions. By integrating video analysis, 3D kinematics estimation, and personalized FE simulation, we extract accurate impact kinematics, QC 20240102

Introduction: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as ‘statistical alerts’) generated is expected. Objectives: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. Methods: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. Results: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. Conclusion: Our approach reduced the nee

Microwave sensors can be used within medicine as they use non-ionizing radiation, are often low cost, and can be designed for a specific purpose. The application of microwave sensors for diagnostics and monitoring can be improved using appropriate data analysis. The multi-layered structure of the human body makes the measurements on people complex. A tremendous effort is required to create an analytical model of the body. In this context a data-driven approach, building a model that learns from previous measurements, is more suitable to analyze the data. This thesis aims to address statistical and data-driven approaches based on microwave sensor data for biomedical applications. A significant part of this thesis deals with microwave sensors for assessing muscle quality. It details the progress from initial clinical campaign to the creation of a machine learning algorithm to assess the local body composition. Such a device would be suitable for screening age-related muscle disorders like sarcopenia and muscle atrophy. Statistical analysis following the initial clinical campaign revealed no significant differences in the microwave data. Therefore, new sensor designs were evaluated. The most promising sensor was used in a small clinical campaign where it was able to detect a change in muscle size for one patient with multiple measurements over time. Successive measurements followed on tissue emulating phantoms and volunteers. For data analysis a machine learning algorithm was designed to predict the skin, fat, and muscle properties. This changes the aim from assessing muscle quality to assessing local body composition. For phantom data the algorithm was accurate for skin and fat and for volunteer data for fat and muscle. Crucially, the algorithm also performed better with more data available, meaning that results should improve if more data is collected. Microwave sensors have also been employed to assess bone. The first of two applications was to monitor the bone heal

A traumatic brain injury is usually caused by a direct impact to the head and is a common cause of disability and death all around the world. The most effective method to predict brain injury today, is to use a finite element head model. In this investigation, the three injury predictors strain, strain rate, and the product of strain and strain rate were investigated using a rat brain finite element model. The main goal was to find which injury predictor most effectively would predict injury. To find the injury predictor with the highest area under curve value, comparisons between experimental results obtained from simulations and results from previously performed experiments on rats were made. To better understand how different factors can affect the severity of symptoms from a traumatic brain injury, a parametric study with a focus on rotational direction and rotational duration was conducted. Simulations were run on a rat brain finite element model for three rotational directions and three rotational durations. The statistical analysis was completed for six experiments and nine brain regions. The three injury predictors were extracted from 26 simulations completed on a rat brain finite element model, and the maximum values of the 95th percentile for each brain region were extracted. The results showed that the product of the strain and the strain rate was the most effective injury predictor for four out of six experiments (unconscious time, EPM arm change, EPM open duration, and MWM session 3). The parametric study investigated rotation in the axial, coronal, and sagittal plane against the three rotational durations 1.5 ms, 3 ms, and 6 ms. The parametric study revealed that both the direction and duration of rotation importantly influence the extent of damage in traumatic brain injuries. The results showed that rotation in the axial plane and a 3 ms duration caused the most brain damage. It was also concluded that the results need to undergo additional verificat, En traumatisk hjärnskada orsakas vanligtvis av våld mot huvudet och är en vanlig orsak till både funktionsnedsättningar och dödsfall världen över. Den effektivastemetoden för att kunna förutsäga en hjärnskada idag är att använda en finit elementmetodmodell av en hjärna. I denna undersökning har de tre skadeprediktorerna belastning, belastningshastighet och produkten av belastningen och belastningshastigheten undersöktes med hjälp av simuleringar genomförda på en modell av en råtthjärna, byggd med hjälp av finita elementmetoden. Målet var att ta reda på vilken skadeprediktor som mest effektivt kunde förutsäga hjärnskada. För att hitta skadeprediktorn med högst area under curve-värde gjordes jämförelser mellan experimentella resultat från simuleringar mot resultat från tidigare utförda experiment på råttor. För att få en djupare förståelse för vilka parametrar som kan påverka graden av symptom från en traumatisk hjärnskada genomfördes en parametrisk studie med fokus på rotationsriktning och rotationstid. Nya simuleringar genomfördes på en finit elementmodell av en råtthjärna i tre rotationsriktningar och under tre rotationstider. Den statistiska analysen utfördes på sex experiment och för nio regioner i hjärnan. Belastningen, belastningshastigheten samt produkten av belastningen och belastningshastigheten extraherades från 26 simulerade finita element råtthjärnor och maximumvärdet från den 95.e percentilen sparades. Resultatet av den statistiska analysen visade att produkten av belastningen och belastningshastigheten var den skadeprediktorn med bäst skadeförutsägelse för fyra av sex experiment(medvetslös tid, EPM arm förflyttning, EPM varaktighet i öppet utrymme och MWM session 3). Under den parametriska studien undersöktes axial, koronal och sagittal rotationsriktning mot de tre rotationstiderna 1.5 ms, 3 ms och 6 ms. Resultatet av den parametriska studien visade att både rotationsriktning och rotationstid spelar viktiga roller när det kommer till omfattningen av s

This study evaluates the usability of the SocioBalance game, a simulation tool designed to assess factors influencing mental well-being among youth. The evaluation focused on three key aspects: effectiveness, efficiency, and satisfaction, measured through a System Usability Scale (SUS) and specific interaction points in the game. The findings revealed significant usability issues, with an average SUS score below industry standards. Key areas such as the menu and certain game sections showed low scores, indicating a need for targeted improvements. The study concludes with recommendations for enhancing the usability of the game to better support its role as an analytical tool for mental health policy development.This is a template for writing bachelor thesis reports for the ICT school at KTH. I do not own any ofthe images provided in the template and this can only be used to submit thesis work for KTH., Denna studie utvärderar användbarheten av SocioBalance-spelet, ett simuleringsverktyg utformat för att bedöma faktorer som påverkar ungdomars mentala välbefinnande. Utvärderingen fokuserade på tre nyckelaspekter: effektivitet, effektivitet och tillfredsställelse, mätt genom ett System Usability Scale (SUS) och specifika interaktionspunkter i spelet. Resultaten visade på betydande användbarhetsproblem, med ett genomsnittligt SUS-poäng under industristandard. Viktiga områden som menyn och vissa speldelar visade låga poäng, vilket indikerar behovet av riktade förbättringar. Studien avslutas med rekommendationer för att förbättra spelets användbarhet för att bättre stödja dess roll som ett analytiskt verktyg för utveckling av mental hälsopolicy.

Artifact detection in electroencephalogram (EEG) recordings is vital for ensuring data qual-ity in neuroscience research. This study evaluates the performance of an artifact detection algorithm on a 100-hour dataset, revealing significant challenges. This study uses Independent Component Analysis (ICA) to detect eye artifacts and Canonical Correlation Analysis (CCA) to identify muscle artifacts in EEG recordings in order to identify timestamps for each detected artifact. The results show that despite challenges, the algorithm achieved 92 percent accuracy in detecting ICA components. Notably, while the ICA method detects eye artifacts with a 58 percent success rate and achieves 58.9 percent in duration detection, the CCA method performs significantly better. The CCA method detects muscle artifacts at a rate of 86.7 percent and has an 84.2 percent in duration detection. Although the two methods target different artifacts, the CCA method demonstrates better detection rates compared to the ICA method when evaluated against annotated artifacts. The complexity arises from the simultaneous occurrence of various artifacts, particularly eye artifacts whose identification is hindered by their variability. The ICA- and CCA method managed to detect the majority of the artifacts. However, the CCA method exhibits promising results in detecting muscle artifacts, leveraging their distinct characteristics to enhance detection accuracy. In conclusion, while both methods are valuable methods, the CCA method gives significantly better results due to lower autocorrelation and higher frequency in muscles, making it easier for the algorithm to detect muscle artifacts.

Hypertension, or high blood pressure, is a prevalent condition that significantly contributes to early mortality worldwide. The World Health Organization (WHO) estimates that 1.28 billion adults aged 30-79 years have hypertension, yet only half are aware of their condition. Hypertension, often asymptomatic, is diagnosed through blood pressure measurements in the larger arteries of the arm, with readings above 140/90 mmHg considered high. If untreated, hypertension can lead to serious health issues such as heart failure, stroke, myocardial infarction, and kidney disease. Commonly prescribed medications for hypertension include angiotensin-converting enzyme (ACE) inhibitors, angiotensin-2 receptor blockers (ARBs), and hydrochlorothiazide (HTZ). This study delves into the mathematical modeling of microcirculatory perfusion and the impact of these blood pressure medications. The aim is to deepen the understanding of microcirculation and how it is influenced by hypertension treatments. The study employs a mathematical model that integrates vascular resistance and compliance across different vessel types, including arterioles, capillaries, and venules, as well as the larger arteries and veins. Two iterations of the perfusion model were developed. The initial version provided a basic understanding but failed to accurately capture the perfusion overshoot observed post-occlusion in the experimental data. An improved second version incorporated additional parameters representing the larger arteries, leading to a more accurate simulation of microcirculatory perfusion. The findings indicate that blood pressure medications influence microcirculatory perfusion by modifying vascular resistance and compliance, particularly in the arterioles and capillaries. However, due to limited amount of data, general conclusions about the impact of the medications cannot be definitively drawn. In conclusion, while the developed model offers valuable insights into microcirculatory dynamics and t

Projektets mål var att träna och optimera en befintlig AI-modell för att skriva medicinska rapporter baserade på mätvärden från ultraljudsundersökningar. Det fanns ungefär 130 tusen undersökningar tillgängliga för träning av modellen. Alla undersökningar rensades på onödiga data och formaterats för att göra dem kompatibla med modellen. AI:n tränades sedan för att lära sig mönster och sammanhang mellan mätvärdena och läkarnas svarstexter. Efter träningsfasen genomfördes tester för att utvärdera modellens prestanda och identifiera områden för förbättring. Genom iterativa optimeringsprocesser justerade vi modellens parametrar och träningsdata för att ytterligare höja dess precision och effektivitet. Dessa justeringar inkluderade både finjustering av olika parametrar samt förbättring av datakvaliteten. Tre läkare har bedömt modellen och givit den 8,9 av 10 för dess språkliga kunskaper och 5,8 för medicinsk korrekthet. AI:n har visat sig otillräcklig att förstå mer komplexa fall men genererar bra texter för standardundersökningar för olika uppföljningar. Med detta kan slutsatsen dras att modellen för närvarande inte är tillräckligt konsekvent för praktiskt användande, däremot ses en potential för förbättring med bättre resurser och mer arbete., The project's goal was to train and optimize an existing AI model to write medical reports based on measurements from ultrasound examinations. There were approximately 130 thousand examinations available for training the model. All examinations were cleaned of unnecessary data and formatted to make them compatible with the model. The AI was then trained to learn patterns and relationships between the measurements and the doctors' response texts. After the training phase, tests were conducted to evaluate the model's performance and identify areas for improvement. Through iterative optimization processes, we adjusted the model's parameters and training data to further enhance its accuracy and efficiency. These adjustments included both fine-tuning various parameters and improving data quality. Three doctors assessed the model and gave it 8.9 out of 10 for its linguistic skills and 5.8 for medical accuracy. The AI has proven insufficient in understanding more complex cases but generates good texts for standard examinations for various follow-ups. This leads to the conclusion that the model is currently not consistent enough for practical use, however there is potential for improvement with better resources and more work.

Elektrofysiologiska katetrar (EP-katetrar) används för att undersöka hjärtats elektriska aktivitet och är viktiga för både diagnostik och behandling. En EP-kateter består av en tunn, flexibel tub gjord av syntetiskt material, med elektroder i den distala änden. Flexibla termoplastmönsterkort (FPCB) eliminerar behovet av att manuellt fästa enskilda komponenter på tuben, som exempelvis elektroder. CathPrint AB har utvecklat en teknik för att fästa FPCB:s runt tuben med en krympslang genom en process som kallas för omsmältning. Trots att tekniken har fungerat bra hittills så är krympslangen en engångsprodukt och omsmältningen utförs för hand vilket är tidskrävande. Företaget arbetar för närvarande på ett verktyg som ska ersätta krympslangens funktion och utesluta behovet av engångsprodukter. Projektet avsåg att vidareutveckla verktyget för att sedan omsmälta katetrar och utvärdera resultaten. Detta gjordes genom att anpassa verktyget till de krav som en omsmältning innebär. Resultaten av omsmältningarna visar att verktyget inte är optimalt utformat. Detta orsakar bland annat att katetrarna utsätts för hög värme under för lång tid och att ett otillräckligt tryck appliceras, vilket skadar FPCB:t. Vi bedömer därför att detta projekt kan utgöra grunden för vidareutveckling av verktyget med en ny design, alternativt genom att implementera ett kylsystem för att uppnå en lyckad omsmältning., Electrophysiological catheters (EP catheters) are used to examine the heart's electrical activity and are essential for both diagnosis and treatment. An EP catheter consists of a thin, flexible tube made of synthetic material, with electrodes at the distal end. Flexible printed circuit boards (FPCBs) eliminate the need to manually attach individual components on the tube, such as electrodes. CathPrint AB has developed a technique to attach FPCBs around the tube using a shrinking tube. This process is called reflow. Although the technique has worked well so far, the shrinking tube is currently a disposable product, and the reflow process is manually performed, making it time consuming. The company is currently working on a tool to replace the function of the shrinking tube and eliminate the need for disposable products. The project focuses on to further develop this tool to then reflow catheters and evaluate the results. This was done by adapting the tool to meet the requirements of the reflow process. The reflow results show that the tool is not optimally designed. This causes, among other things, the catheters to be exposed to high heat for too long and insufficient pressure is applied, damaging the FPCB. We therefore find it that this project can serve as a foundation for further development of the tool with either a new design or by implementing a cooling system to achieve successful reflow.

Traumatic brain injury (TBI) is a worldwide public health problem. It is often caused by impacts to the head, which can cause translational and rotational motions. During impacts to the head, the neck serves as one of the boundary conditions for determining its kinematic response. In today’s helmet assessment standards, the dynamics of the neck are not included, and in most standards only translational accelerations are examined within a short time interval around 20- 30 ms. However, to understand the risk of brain injury, it is also important to account for the rotational motions and the influence of the neck on head kinematics. In this thesis the influence of the neck on head kinematics was investigated by comparing 5 different finite element (FE) models of the human. By using finite element analysis, simulations of four different accident scenarios were conducted. Most models are produced for the automotive industry and are not validated in vertical impacts with forces acting on the head. The accident scenarios included vertical and horizontal impacts to the head with different striking objects. The models included two anthropomorphic test devices (ATD) and three human body models (HBM). Furthermore, an isolated head was also used. The models were equipped with an industrial safety helmet, with and without a low friction layer (LFL). Additionally, the helmet versions were used to investigate how the various FE models predict the difference in rotational kinematics. The head kinematics showed considerable disparities between the ATDs and the HBMs. The ATDs mostly showed a stiffer, spring-like behavior with higher translational accelerations and lesser rotational motions. Furthermore, the HBMs showed responses that were assumed to have been in better proximity to biofidelic responses. The incorporation of the LFL led to a reduction in peak resultant rotational velocity (PRV) in most models and accident scenarios. Furthermore, the results were highly influenced by, Traumatiska hjärnskador är ett globalt folkhälsoproblem. Traumatiska hjärnskador orsakas ofta av slag mot huvudet, vilket kan orsaka både translations- och rotationsrörelser av hjärnan. Vid slag mot huvudet verkar halsen som ett av randvillkoren som styr kinematiken av huvudet. I dagens hjälmstandarder är halsens dynamik inte inkluderad och i majoriteten av standarder är det endast translationsaccelerationer som undersöks samt inom en kort tidsram, runt 20-30 ms. För att förstå risken för hjärnskador behöver man även beakta rotationsrörelser och då blir halsens inflytande på huvudets kinematik av vikt, liksom att utvärdera kinematiken under en längre tid. I detta examensarbete studeras halsens inverkan på huvudets kinematik genom att jämföra fem olika finita element (FE) modeller av människan. Genom att använda finita elementmetoden, genomfördes simuleringar av 4 olika olycksscenarior. Olycksscenariorna inkluderade vertikala och horisontella islag med olika objekt. De modeller som användes var två krockdockor och tre humanmodeller samt ett isolerat huvud. De flesta modeller är framtagna för bilindustrin vilket påverkar dess användningsområde genom begränsade valideringar av vertikala slag med krafter som verkar direkt på huvudet. Alla modeller var utrustade med en industrihjälmsmodell, med respektive utan ett lågfriktionslager. Dessutom användes hjälmmodellerna till att undersöka hur de olika FE modellerna förutspådde skillnader i rotationskinematik. Kinematiken av huvudet visade på signifikanta skillnader mellan krockdockorna och humanmodellerna. Krockdockorna hade generellt ett stelare, fjäderliknande beteende med högre translationsacceleration och mindre rotationsrörelse. Vidare hade humanmodellerna ett beteende som var mer likt den förväntade mänskliga responsen. Användandet av lågfriktionslagret ledde till reduktion i resulterande peak rotationshastighet bland de flesta modeller och olycksscenarior. Result

In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project. In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance. This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are nec

Atopic dermatitis (AD) and other atopic diseases are strongly related to skinbarrier dysfunction, a biomeasure which has limited and unsatisfactory assessmenttechniques. Machine learning (ML) powered electrical impedance spectroscopy(EIS) has been shown to differentiate defective barrier function in mice and adults. Techniques such as principal component analysis (PCA) andsupport vector classifiers (SVC) can be used as ML tools to evaluate EIS measurements. EIS measurements taken on unaffected skin of children aged between 4 monthsand 3 years were collected and analysed using the children’s AD status. Measurements were grouped into one of four groups based on this AD status; No AD (No AD was developed up until 2 years of age), Pre AD (measurements takenbefore the onset of AD), AD remission (measurements taken after the onset ofAD) and AD flare (measurements taken during active AD). A SVC model was trained on the raw EIS measurement data to distinguish measurements from twoof the binarized AD status groups; AD flare and No AD. An additional SVC model was trained on the No AD group, distinguishing measurements based onbinarized age groups: measurements taken at 4 months against measurements taken at 3 years of age. The AD model tested on AD flare against No AD within the test set yielded AUC of 0.92, with a sensitivity and specificity of 89.29% and 88.89% respectively. When testing on all groups of the test set, Pre AD and AD remission groups had group means between the AD flare and No AD groups. No data bias against age was detected in the model. The results of the age model showed that age could be chronologically identified by the age model. The AD model was able to differentiate active AD children from children never experiencing AD symptoms on visually unaffected skin and in turn detecting skin barrier dysfunction. Separate studies would need to be conducted to test the predictive power and external validity of the model. Age is a significant factor to consider w, Atopisk dermatit (AD) och andra atopiska sjudomar är starkt kopplade tillhudbarrärdefekter, en hudegenskap där diagnosverktygen är begränsade och otillräckliga. Maskininlärning (ML) i kombination med elektrisk impedansspektroskopi (EIS) har visats att kunna differentiera defekta hudbarrärer i möss och hos vuxna människor. Tekniker såsom principalkomponentanalys (PCA) och stödvektormaskiner (SVM) kan användas som ML verktyg för att utvärdera EIS-mätningar. Statistiska analysverktyg såsom reciever operator characteristics (ROC) tillsammans med arean under kurvan (AUC) och andra precisionsmått kan användas för att utvärdera ML modeller. EIS-mätningar utförda på opåverkad hud hos barn mellan 4 månader och 3 års ålder samlades och analyserades med hjälp av barnens atopiska status. Samtliga mätningarna grupperades i en av totalt fyra grupper baserade på deras atopiska status; Ingen AD (Ingen AD hade utvecklats upp till 2 års ålder), Pre AD (mätningarna togs innan sjukdomsförloppet), AD remission (mätningarna togs efter sjukdomsförloppet) och AD aktiv (mätningarn togs under sjukdomsförloppet). En SVM modell tränades på EIS-rådata för att särskilja mellan två av grupperna binärt; AD aktiv och ingen AD. En ytterliggare SVM modell tränades på endast ingen AD-gruppen för att särskilja mellan mätningarna tagna vid åldrarna 4 månader och 3 år, omgjorta till binära grupper. AD modellen testades med en jämförelse mellan AD aktiv-gruppen och ingen AD-gruppen med test data och resulterade i en AUC på 0.92, med en respektive sensitivitet och specificitet på 89.29\% samt 88.89\%. Modellen testades även på de ytterliggare grupperna i test datan, Pre AD och AD remission som båda hade gruppmedelvärden som låg mellan AD aktiv-gruppens och ingen AD-gruppens. Inga systematiska fel med avseende på barnens ålder hittades i AD-modellen. Resultaten från ålders-modellen visade på att modellen kunde rangordna mätningarna baserat på ålder. AD-modellen kunde differentiera barn med aktiv AD mot bar

In the manufacturing industry, predictive maintenance (PdM) stands out by leveraging data analytics and IoT technologies to predict machine failures, offering a significant advancement over traditional reactive and scheduled maintenance practices. The aim of this thesis was to examine how anomaly detection algorithms could be utilized to anticipate potential breakdowns in manufacturing operations, while also investigating the feasibility and potential benefits of integrating PdM strategies into a production line. The methodology of this projectconsisted of a literature review, application of machine learning (ML) algorithms, and conducting interviews. Firstly, the literature review provided a foundational basis to explore the benefits of PdM and its impact on production line productivity, thereby shaping the development of interview questions. Secondly, ML algorithms were employed to analyze data and predict equipment failures. The algorithms used in this project were: Isolation Forest (IF), Local Outlier Factor (LOF), Logistic Regression (LR), One-Class Support Vector Machine(OC-SVM) and Random Forest (RF). Lastly, interviews with production line personnel provided qualitative insights into the current maintenance practices and perceptions of PdM. The findings from this project underscore the efficacy of the IF model in identifying potential equipment failures, emphasizing its key role in improving future PdM strategies to enhance maintenance schedules and boost operational efficiency. Insights gained from both literature and interviews underscore the transformative potential of PdM in refining maintenance strategies, enhancing operational efficiency, and minimizing unplanned downtime. More broadly, the successful implementation of these technologies is expected to revolutionize manufacturing processes, driving towards more sustainable and efficient industrial operations., I tillverkningsindustrin utmärker sig prediktivt underhåll (PdM) genom att använda dataanalys och IoT-teknologier för att förutse maskinfel, vilket erbjuder ett betydande framsteg jämfört med traditionella reaktiva och schemalagda underhållsstrategier. Syftet med denna avhandling var att undersöka hur algoritmer för avvikelsedetektering kunde användas för att förutse potentiella haverier i tillverkningsoperationer, samtidigt som genomförbarheten och de potentiella fördelarna med att integrera PdM-strategier i en produktionslinje undersöktes. Metodologin för detta projekt bestod av en litteraturöversikt, tillämpning av maskininlärningsalgoritmer (ML) och genomförande av intervjuer. Först och främst gav litteraturöversikten en grundläggande bas för att utforska fördelarna med PdM och dess inverkan på produktionslinjens produktivitet, vilket därmed påverkade utformningen av intervjufrågorna. För det andra användes ML-algoritmer för att analysera data och förutsäga utrustningsfel. Algoritmerna som användes i detta projekt var: Isolation Forest (IF), Local Outlier Factor (LOF), Logistic Regression (LR), One-Class Support Vector Machine (OCSVM) och Random Forest (RF). Slutligen gav intervjuer med produktionslinjepersonal kvalitativa insikter i de nuvarande underhållsstrategierna och uppfattningarna om PdM.Resultaten från detta projekt understryker effektiviteten hos IF-modellen för att identifiera potentiella utrustningsfel, vilket betonar dess centrala roll i att förbättra framtida PdM-strategier för att förbättra underhållsscheman och öka den operativa effektiviteten. Insikter vunna från både litteratur och intervjuer understryker PdM:s transformativa potential att finslipa underhållsstrategier, öka operativ effektivitet och minimera oplanerade driftstopp. Mer generellt förväntas den framgångsrika implementeringen av dessa teknologier revolutionera tillverkningsprocesser och driva mot mer hållbara och effektiva industriella operationer.

While markerless motion capture provided acceptable accuracy, no clear patterns emerged regarding the individual effects of surface properties on technique. This is most likely due to limitations such as sample size, lack of standardizing data set (players) across facilities, and limited control over player behavior. However, analyzing one individual's motion capture data across surfaces showed potential for distinguishing turning styles based on facility parameters. The method in this thesis demonstrates the potential of markerless motion capture for injury prevention research in football. Despite inconclusive results on the individual facility parameter effects, the ability to distinguish player styles across surfaces suggests valuable future directions for investigating personalized risk factors and optimizing playing surfaces. Further research with larger, more diverse samples and a broader set of biomechanical and facility features could provide deeper insight into injury prevention strategies.

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

Background Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique.Methods We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately.Results Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements.Conclusions This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare. This research explores a new way to monitor health using video, which is less invasive than traditional methods that require direct skin contact. We developed a computer program that improves the accuracy of heart signals captured from video. This is done by comparing these video-based signals with standard clinical signals from physical sensors on the skin. Our findings show that this new method can match the accuracy of conventional clinica, QC 20240626

In real-world car to vulnerable road user (VRU) impact accidents, head impactisthe leading cause of VRU injuryanddeath. The currently used head impactor test used in pedestrian safety regulations/programs only considers the simple impact conditions that can not fully reflect the real impact event, and the used head injury criterion HIC onlyconsiders head liner acceleration. Meanwhile, specific testing and evaluation program for remaining VRU protection still needs to be developed.To explore the head injury mechanism from a more realistic and comprehensive perspective, 50 real-world car-to-VRU impact accident cases were selected and reconstructed numerically, through which the boundary conditions of the impact between VRUs’ head and vehicle as well as the head injury parameters during the impact events were obtained. Using linear regression model, the correlations between impact kinematics condition parameters of the head and predictions based on various head injury criteria were comprehensively investigated.The results show that a significant correlation betweenthe linear acceleration, rotational acceleration, velocity and rotational velocity and the head injury was observed. The headimpact rotational kinematics has a higher correlation with the predicted brain injury parameters than head linear kinematics, while the impact duration has no significant correlation. The result also indicated that the selection of head injury criterion needs to depend on the severity of impact boundary conditions. The current study could be an important reference for expanding the pedestrian safety tesing scenarios and determing the head injury criteria used for the head injury risk evluation., QC 20240925

Finite element (FE) modeling provides a means to examine how global kinematics of repetitive head loading in sports influences tissue level injury metrics. FE simulations of controlled soccer headers in two directions were completed using a human head FE model to estimate biomechanical loading on the brain by direction. Overall, headers were associated with 95th percentile peak maximum principal strains up to 0.07 and von Mises stresses up to 1450 Pa, and oblique headers trended toward higher values than frontal headers but below typical injury levels. These quantitative data provide insight into repetitive loading effects on the brain., QC 20240711

Venovenous extracorporeal membrane oxygenation is used for respiratory support in the most severe cases of acute respiratory distress syndrome. Blood is drained from the large veins, oxygenated in an artificial lung, and returned to the right atrium (RA). In this study, we have used large eddy simulations to simulate a single-stage “lighthouse” drainage cannula in a patient-averaged model of the large veins and RA, including the return cannula. We compared the results with previous experimental and numerical studies of these cannulas in idealized tube geometries. According to the simulations, wall proximity at the drainage holes and the presence of the return cannula greatly increased drainage through the tip (33% at 5 L/min). We then simulated a multi-stage device in the same patient-averaged model, showing similar recirculation performance across the range of extracorporeal membrane oxygenation (ECMO) flow rates compared to the lighthouse cannula. Mean and maximum time-averaged wall shear stress were slightly higher for the lighthouse design. At high ECMO flow rates, the multi-stage device developed a negative caval pressure, which may be a cause of drainage obstruction in a clinical environment. Finally, through calculation of the energy spectra and vorticity field, we observed ring-like vortices inside the cannula originating from the side holes, most prominent in the proximal position. Our work highlights the important differences between a patient-derived and simplified venous model, with the latter tending to underestimate tip drainage. We also draw attention to the different dynamics of single-stage and multistage drainage cannulas, which may guide clinical use., QC 20240703

Magnetic hyperthermia holds significant therapeutic potential, yet its clinical adoption faces challenges. One obstacle is the large-scale synthesis of high-quality superparamagnetic iron oxide nanoparticles (SPIONs) required for inducing hyperthermia. Robust and scalable manufacturing would ensure control over the key quality attributes of SPIONs, and facilitate clinical translation and regulatory approval. Therefore, we implemented a risk-based pharmaceutical quality by design (QbD) approach for SPION production using flame spray pyrolysis (FSP), a scalable technique with excellent batch-to-batch consistency. A design of experiments method enabled precise size control during manufacturing. Subsequent modeling linked the SPION size (6–30 nm) and composition to intrinsic loss power (ILP), a measure of hyperthermia performance. FSP successfully fine-tuned the SPION composition with dopants (Zn, Mn, Mg), at various concentrations. Hyperthermia performance showed a strong nonlinear relationship with SPION size and composition. Moreover, the ILP demonstrated a stronger correlation to coercivity and remanence than to the saturation magnetization of SPIONs. The optimal operating space identified the midsized (15–18 nm) Mn0.25Fe2.75O4 as the most promising nanoparticle for hyperthermia. The production of these nanoparticles on a pilot scale showed the feasibility of large-scale manufacturing, and cytotoxicity investigations in multiple cell lines confirmed their biocompatibility. In vitro hyperthermia studies with Caco-2 cells revealed that Mn0.25Fe2.75O4 nanoparticles induced 80% greater cell death than undoped SPIONs. The systematic QbD approach developed here incorporates process robustness, scalability, and predictability, thus, supporting the clinical translation of high-performance SPIONs for magnetic hyperthermia.

Objective. This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Approach. Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise. Main results. Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance. Our findings showcase the promising potential of EEG-based tools for assessing, Funding Agencies|ELLIIT

In modern hospitals, monitoring patients’ vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration., QC 20231218

Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson’s correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment., QC 20240122

Cyclists are vulnerable road users and often suffer head-neck injuries in car–cyclist accidents. Wearing a helmet is currently the most prevalent protection method against such injuries. Today, there is an ongoing debate about the ability of helmets to protect the cyclists’ head-neck from injury. In the current study, we numerically reconstructed five real-world car–cyclist impact accidents, incorporating previously developed finite element models of four cyclist helmets to evaluate their protective performances. We made comparative head-neck injury predictions for unhelmeted and helmeted cyclists. The results show that helmets could clearly lower the risk of severe (AIS 4+) brain injury and skull fracture, as assessed by the predicted head injury criterion (HIC), while a relatively limited decrease in AIS 4+ brain injury risk can be achieved in terms of the analysis of CSDM0.25. Assessment using the maximum principal strain (MPS0.98) and head impact power (HIP) criteria suggests that helmets could lower the risk of diffuse axonal injury and subdural hematoma of the cyclist. The helmet efficacy in neck protection depends on the impact scenario. Therefore, wearing a helmet does not seem to cause a significant neck injury risk level increase to the cyclist. Our work presents important insights into the helmet’s efficacy in protecting the head-neck of cyclists and motivates further optimization of protective equipment., QC 20231113

Background: The internet of things (IoT) is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. This study aims to identify the facilitators and barriers associated with implementing IoT solutions in health care within a Scandinavian context. It addresses the pressing need to adapt health care systems to the demographic changes occurring in Scandinavia. The vision of “Vision eHealth 2025,” a long-term strategic direction for digitalization in Sweden, serves as the background for this project. The implementation of IoT solutions is a crucial aspect of achieving the vision’s goal of making Sweden a global leader in using digitalization and eHealth opportunities by 2025. IoT is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. Previous research has shown that there is a gap in our understanding of social and organizational challenges related to IoT and that the implementation and introduction of new technology in health care is often problematic. Objective: In this study, we will identify facilitating and hindering factors for the implementation of IoT solutions in social and health care. Methods: We will use an explorative design with a case study approach. The data collection will comprise questionnaires and qualitative interviews. Also, a literature review will be conducted at the start of the project. Thus, quantitative and qualitative data will be collected concurrently and integrated into a convergent mixed methods approach. Results: As of June 2023, data for the review and 22 interviews with the stakeholders have been performed. The co-design with stakeholders will be performed in the fall of 2023. Conclusions: This study represents a unique and innovative opportunity to gain new knowledge relevant and useful for future implementation of new technology at health care organizations so they can continue to offer high-quality, person-centered care. The outcom

Around 10% of women in fertile age have endometriosis, despite this there is little known about its origin. It can take years from the first experienced symptoms to an established diagnosis, which is done using invasive methods. A database from the Lucy application containing 11720 questionnaires with up to 41 questions each was examined for signs of associations between lifestyle factors and health indicators with endometriosis. The database was restructured and unusable questionnaires were pruned, leaving 5719 questionnaires for analysis. Questionnaires were assigned a label depending on whether the respondent had an endometriosis diagnosis, some other diagnosis or no diagnosis. Histograms were created for 36 questions to visualize answer distributions among the labels while wordclouds were created for 3 questions to determine commonly used words. Pearson’s chi-square test was performed on 24 questions to determine whether there were statistically significant differences in how respondents answered with regards to their diagnosis. Then, 26 questions were divided between seven question groups based on their similarities. The relative prevalence of endometriosis for every answer alternative was determined and compared withthe average for that group. This was then followed up by a multi-correspondence analysis for every question group. It was found that there are associations between endometriosis and 12 health indicators. These being: affected by infertility, always being tired, regarding own health as bad, having more than 100 birthmarks, severe chronic pelvic pain, dysmenorrhea, regularly whole-body pain, muscle stiffness, neck and shoulder tension, forgetfulness, concentration difficulties, regularly experiencing that stress worsens physical symptoms. No statistically significant association between lifestyle factors and endometriosis could be found in the survey database. There were findings of socioeconomic differences between respondents with endometriosis and, Runt 10% av kvinnor i fertil ålder har endometrios, trots detta är lite känt om dess ursprung. Det kan gå flera år från första upplevda symptom till en etablerad diagnos, som kräver invasiva metoder. En databas från Lucy applikationen med 11 720 enkäter upp till 41 frågor vardera undersöktes efter tecken på kopplingar mellan livsstilsfaktorer och hälsoindikatorer med endometrios. Som ett första steg omstrukturerades databasen och oanvändbara enkäter sållades bort, vilket lämnar 5719 enkäter för analys. Enkäterna blev tilldelad en etikett baserad på huruvida respondenten hade en endometriosdiagnos, någon annan diagnos eller ingen diagnos. Histogram skapades för 36 frågor för att visualisera svarsfördelningen mellan etiketter medan ordmoln skapades för 3 frågor för att bestämma vanligt förekommande ord. Pearsons chi-kvadrattest utfördes på 24 frågor för att bestämma huruvida det fanns signifikanta statistiska skillnader i hur respondenter svarade med avseende på deras diagnos. 26 frågor blev uppdelade i 7 olika grupper baserade på deras liknelser och den relativa utbredningen av endometrios bestämdes för varje grupp och jämfördes med snittet i varje grupp. Slutligen följdes detta upp med multikorrespondensanalys för varje grupp av frågor. Det hittades associationer mellan endometrios och 12 hälsofaktorer. Dessa var: infertilitet, alltid vara trött, betrakta sin egna hälsa som dålig, ha fler än 100 födelsemärken, uppleva allvarlig kronisk bäckensmärta, dysmenorré, regelbunden helkroppssmärta, muskelstelhet, spändhet i nacke och axlar, glömskhet, koncentrationssvårigheter, regelbundet uppleva att stress förvärrar fysiska symptom. Ingen statistisk signifikant association kunde hittas mellan livsstilsfaktorer och endometrios från undersökningsdatabasen. Det hittades socioekonomiska skillnader mellan respondenter med endometrios och de utan diagnos vilket kan ha påverkat resultatet., FEMaLe

Denna rapport presenterar en utvärdering av en centrifugal luftrenare från företaget Airission som användes i en infektionsavdelning på Karolinska Universitetssjukhuset. Airissions luftrenare rensar bort partiklar och aerosoler, genom så kallad centrifugalteknologi, en relativt oprövad teknologi för luftrening. Målet var att undersöka och försöka verifiera luftrenarens funktion och prestanda samt jämföra den med en konventionell luftrenare som använder traditionella tvåstegsfilter för att rena luft.För att utföra studien användes ett bioaerosolmätinstrument som i realtid mäter partikelnivåerna i rummet. Testerna utfördes under olika driftförhållanden och tidsintervall med luftrenare påslagen respektive avstängd. Datainsamlingen och analysen inkluderade beräkningen av medelvärdet, jämförelse av partikelnivåerna mellan de olika testfallen och beräkning av standardavvikelsen.Resultaten visade att Airissions centrifugala luftrenare fungerar väl för rening av luft från partiklar och aerosoler. Reningseffektiviteten var jämförbar med en konventionell luftrenare. Den var mer effektiv än en konventionell luftrenare utan användning av högeffektiva luftfilter, mer allmänt känt som HEPA-filter. En signifikant minskning av antalet partiklar i luften observerades medan luftrenaren var i drift. Det uppstod dock en del komplikationer vid appliceringen av HEPA-filtret, vilka kunde ha haft en negativ påverkan på båda luftrenarna. Sammanfattningsvis visar analysen att den tillämpade centrifugalteknologin hos Airissions luftrenare fungerar väl. Jämförelsen av den konventionella luftrenaren visar vissa fördelar med en luftrenare som använder centrifugal teknologi - både gällande effektivitet och kvalitet.Denna rapport bidrar till kunskapen om luftreningslösningar för att förbättra luftkvaliteten och minska spridningen av luftburna sjukdomar, framför allt i sjukhusmiljöer. Resultaten kan vara till nytta för fortsatt forskning och utveckling av mer effektiva luftrenare som i sin tur ger, This report presents an evaluation of a centrifugal air purifier from the company Airission used in an infectious ward at Karolinska University Hospital. Airission's air purifier removes particles and aerosols using centrifugal technology, a relatively untested technology for air purification. The goal was to investigate and attempt to verify the functionality and performance of the air purifier and compare it to a conventional air purifier that uses traditional two-stage filters for air purification.To conduct the study, a bioaerosol measuring instrument was used to measure real-time particle levels in the room. The tests were performed under different operating conditions and time intervals with the air purifier turned on and off. Data collection and analysis included calculating the mean values, comparing the particle levels between different test cases, and calculating the standard deviation.The results showed that Airission's centrifugal air purifier effectively purifies air from particles and aerosols. The purification efficiency was comparable to a conventional air purifier. It was more effective than a conventional air purifier without the use of highefficiency air filters, commonly known as HEPA filters. A significant reduction in the number of particles in the air was observed while the air purifier was in operation. However, some complications arose during the application of the HEPA filter, which could have had a negative impact on both air purifiers.In summary, the analysis demonstrates that the applied centrifugal technology in Airission's air purifier works well. The comparison with the conventional air purifier shows certain advantages of an air purifier that uses centrifugal technology - in terms of both efficiency and quality.This report contributes to the knowledge of air purification solutions to improve air quality and reduce the spread of airborne diseases, especially in hospital environments. The results can be useful for further research and

In conjunction with the always on-going technology development technical education have been created increasingly. Not least in the biomedical engineering area, which since 2008 has been established in Sweden. Between education and work, it can be assumed that there is a knowledge gap, which means that the workplace needs to supplement the previous studies with internal training. The following work analyses knowledge gaps in newly employed engineers in relation to what skills their working place requires. Furthermore, the study focuses on how the Akademiskas hospitals biomedical engineering department can work with internal training to introduce new employees. The result shows that the knowledge gaps differ greatly among individuals, but the common denominator is mainly applying the knowledge practically and electrical safety. Therefore, to create a flexible educational material to use in internal training of new employees, priority checklists are made which are divided into subject categories. The new employee thus gets the opportunity to practice his knowledge practically as well as theoretically. The theoretical material is created digitally and goes through the subject's underlying theory, demonstration of handling and training material, and the aim is that by letting the new employees go through this, the understanding and motivation of future tasks increases. Hopefully, by repeating the subject theoretical as well as practical the new employee will have a greater shot to process the task in his long-term memory and get a more covering and safer introduction of the workplace., Universitet och högskolor har som skyldighet att skapa utbildningar baserade på konstnärliga eller vetenskapliga grunder som kan bidra till samhällsnytta. År 2008 skapades den första ingenjörsutbildningen i medicinsk teknik efter ett nära samarbete med industri och sjukhus. Syftet var att efter efterfrågan skapa en utbildning som gav studenterna specialkompetens inom medicin i samband med teknik för dagens alltmer avancerade sjukhusteknik. Följande arbete utför en så kallad ”gapanalys” på kunskaperna hos en medicinteknisk ingenjörsstudent kontra en medicinteknisk ingenjör i arbetslivet på ett sjukhus. Vidare undersöker studien hur Akademiska sjukhuset kan arbeta med dessa kunskapsglipor vid anställning av exempelvis tidigare studenter för att få kompetenta ingenjörer som bidrar till arbete enligt den medicintekniska avdelningens uppdrag. Rapporten innefattar en förstudie och en studie där resultatet visar att det förekommer en stor variation på kunskapsbristerna mellan studenterna. Slutsatsen som däremot går att dra från studierna är att den stora kunskapsglipan ligger i att många ingenjörer aldrig har fått tillämpa den teoretiska kunskapen praktiskt. Många anser även att avsaknaden av ett elsäkerhetsmässigt tänk är ett stort och relativt utbrett problem. Särskilt med tanke på hur farligt ett sådant fel kan bli i sjukhusmiljö. I förstudien svarar nio tidigare handledare på sjukhus runtom i landet på en enkät om vad de anser att studenterna som varit där på verksamhetsförlagd utbildning har haft svårt för. Många anser att kunskaperna kan variera mycket beroende på studentens intresse och bakgrund. Förstudien efterföljs av en intervjustudie på Akademiska sjukhuset som även den visar på en stor individuell kunskapsvariation hos studenter över olika ämnen. För att den medicintekniska avdelningen på Akademiska sjukhuset ska kunna skapa kompletterande utbildningar som täcker deras behov på kunskap skapas därför checklistor som även fungerar som en prioriteringsordni

Brain tumors pose a big challenge in the field of neuro-oncology. Gliomas are the largest subgroup. Magnetic resonance imaging is a non-invasive tool for detecting and characterizing these tumors. Mathematical models, such as the reaction-diffusion equation, can be used for understanding the intricate behavior of gliomas. This thesis aims to improve the understanding and prediction of tumor growth by modifying and evaluating a reaction-diffusion mathematical model and incorporating magnetic resonance (MR) images, namely intensity from T1-weighted and T2-weighted images, and apparent diffusion coefficient values. Image data from five patients was used. The finite difference method was used to approximate the solution, and brain segmentation is performed using the software package FSL. The Jaccard index is used to compare the simulation results with the ground truth, being the segmented tumor area. A spatially varying proliferation rate is introduced and histology images are used to construct an initial condition for the reaction-diffusion mathematical model. The results show improvement in performance based on the Jaccard index, with the highest values achieved when using a diffusion matrix given as an affine function of intensity in T1-weighted images. Incorporating a spatially varying proliferation rate reduces the number of iterations required to reach the maximum Jaccard index compared to a constant proliferation rate, but this does not influence the simulation time. The introduction of the p-Laplace operator, particularly with a value of p = 1.8 instead of the usual Laplace operator (where p = 2), leads to a higher Jaccard index, indicating an improvement in the model’s performance. The best Jaccard index achieved was 0.4909 with p = 1.8 compared to the basic model (JI = 0.4382 with p = 2). An initial tumor cell density is constructed using histology images. In conclusion, insights are provided into improving tumor growth modeling by incorporating MR images, the

Featured Application: The results proposed a new approach to evaluate the protection effectiveness of energy-absorbing floors for fall-related injury prevention. Also, it could help to reduce the huge associated costs related to fall-related injuries among the children and elderly population. Energy-absorbing floor (EAF) has been proposed as one of several biomechanically effective strategies to mitigate the risk of fall-related injuries by decreasing peak loads and enhancing system energy absorption. This study aims to compare the protective capacity of four commercially available EAF products (Igelkott Floor, Kradal, SmartCells, and OmniSports) in terms of head impacts using the finite element (FE) method. The stress–strain curves acquired from mechanical tests were applied to material models in LS-Dyna. The established FE models were then validated using Hybrid III or hemispheric drop tests to compare the acceleration–time curves between experiments and simulations. Finally, the validated FE models were utilized to simulate a typical pedestrian fall accident scenario. It was demonstrated that EAFs can substantially reduce the peak forces, acceleration, and velocity changes during fall-related head impacts. Specifically, in the accident reconstruction scenario, SmartCells provided the largest reduction in peak linear acceleration and skull fracture risk, while Igelkott Floor provided the largest reduction in peak angular velocity and concussion risk. This performance was caused by different energy absorption mechanisms. Consequently, the results can contribute to supporting the implementation of EAFs and determine the effectiveness of various protective strategies for fall-related head injury prevention., QC 20240517

Deep brain stimulation (DBS) is an established method for symptom relief in movement disorders like Parkinson’s disease, essential tremor (ET), and dystonia. The therapy is based on implanting an electrode with four contacts in the deep brain structures where it provides electrical stimulation, mainly impacting the nerve tracts. Despite the evidence of DBS effectiveness, there are still questions regarding the optimal position of stimulation. With new technology, the possibility to customize the stimulation increases, which makes the programming session for each patient more complicated and tedious. Different computational models have been developed to estimate the anatomical impact of stimulation. Patient‐specific electric field simulations can be used to estimate the spatial extent of the stimulation and superimpose on patient magnetic resonance imaging (MRI) for anatomical analysis. MRI weighted with water diffusion can be used for reconstructions of nerve tracts, a process called tractography. Tractography utilizes the fact that water can move unrestricted along the nerve trajectories, but the diffusion is restricted in the perpendicular direction, i.e., the diffusion is anisotropic. For tremor, the dentato‐rubro‐thalamic tract (DRT) has gained interest. The electric conductivity has corresponding anisotropic characteristics as water diffusion in white brain tissue (nerve tracts). Diffusion MRI can therefore also be used to improve patientspecific simulations by including structure information, i.e., anisotropy. In this thesis, both a workflow for combining patient‐specific simulations with tractography of the DRT and a method for expanding the simulations with anisotropy were developed (Paper I). This was done using four patients with ET. The results show that including anisotropy will impact the simulation result in regions of dense nerve tracts (Paper I‐II). For the tractography, all patients’ estimated stimulation region intersected with the reconstructed DR

We propose that a recently discovered event-related potential (ERP) component—the pre-activation negativity (PrAN)—indexes the predictive strength of phonological cues, including segments, word tones, and sentence-level tones. Specifically, we argue that PrAN is a reflection of the brain’s anticipation of upcoming speech (segments, morphemes, words, and syntactic structures). Findings from a long series of neurolinguistic studies indicate that the effect can be divided into two time windows with different possible brain sources. Between 136 and 200 ms from stimulus onset, it indexes activity mainly in the primary and secondary auditory cortices, reflecting disinhibition of neurons sensitive to the expected acoustic signal, as indicated by the brain regions’ response to predictive certainty rather than sound salience. After ~200 ms, PrAN is related to activity in Broca’s area, possibly reflecting inhibition of irrelevant segments, morphemes, words, and syntactic structures.

Ultrasound contrast agents consist of gas-filled micrometer-sized bubbles that are injectedinto the blood stream. Ultrasound contrast agents are an invaluable tool for ultrasound imaging of the cardiac muscle and highly vascularized structures such as kidneys and the liver. The ability of gas-filled microbubbles to enhance contrast in ultrasound imaging comes from their increased scattering ability due to significantly lower compressibility compared to surrounding soft tissues. The discovery of acoustic droplet vaporization, the phase-transition of liquid-filleddroplets into gas-filled microbubbles upon ultrasound exposure, has expanded the potential utility of ultrasound-mediated diagnostics and therapy to include applications such as gas embolization, histotripsy, and localized drug delivery. Multiple requirements are put onto both gas-filled microbubbles and phase-change contrast agents: they have to be non-toxic, acoustically active at clinically relevant pressure amplitudes, and their dynamic behavior has to be predictable to maximize the therapeutic or diagnostic effect while minimizing mechanical damage to surrounding healthy tissue. Novel designs of phase-change contrast agents that are able to undergo acoustic droplet vaporization could enable improved in vivo stability compared to conventional gas-filled ultrasound contrast agents. Pickering emulsions, with solid particles used as stabilizing agents instead of surfactants, have an increased stability compared to conventional emulsions. Cellulose-based Pickering emulsions in particular have previously been investigated for biomedical applications. Cellulose is a suitable material in biomedical applications as it originates from renewable sources, is biocompatible, and the surface can be easily modified. To the author’s current knowledge, cellulose-based Pickering emulsions have not previously been investigated for ultrasound-mediated applications. It is necessary to know the mechanical and acoustic properti, Kontrastmedel för ultraljud består av gasfyllda mikrometerstora bubblor som injiceras in i blodomloppet. Kontrastmedel för ultraljud är ett ovärderligt verktyg för ultraljudsavbildning av hjärtmuskeln och starkt vaskulariserade strukturer som njurar och lever. De gasfyllda mikrobubblorna har en förmåga att förstärka kontrasten vid ultraljudsavbildning. Denna förmåga beror på mikrobubblornas ökade spridningsförmåga som i sin tur beror på deras betydligt lägre kompressibilitet jämfört med omgivande mjukavävnader. Upptäckten av akustisk droppförångning, fasövergången av vätskefyllda droppar till gasfyllda mikrobubblor vid ultraljudsexponering, har utökat den potentiella användbarheten av ultraljudsmedierad diagnostik och terapi till att omfatta tillämpningar som gasembolisering, histotripsi och lokal läkemedelstillförsel. Flera krav ställs på både gasfyllda mikrobubblor och kontrastmedel med fasförändring: de måste vara giftfria, akustiskt aktiva vid kliniskt relevanta tryckamplituder och deras dynamiska beteende måste vara förutsägbart för att maximera den terapeutiska eller diagnostiska effekten samtidigt som mekanisk skada på omgivande frisk vävnad minimeras. Nya formuleringar av kontrastmedel som kan genomgå akustisk droppförångning skulle kunna möjliggöra förbättrad stabilitet in vivo jämfört med konventionella kontrastmedel i form av gasfyllda mikrobubblor. Pickeringemulsioner, där fasta partiklar används som stabiliseringsmedel istället för tensider, har en ökad stabilitet jämfört med konventionella emulsioner. Särskilt cellulosabaserade Pickeringemulsioner har tidigare undersökts för biomedicinska tillämpningar. Cellulosa är ett lämpligt material för biomedicinska tillämpningar eftersom det kommer från förnybara källor, är biokompatibelt och ytan lätt kan modifieras. Såvitt författaren vet har cellulosabaserade Pickeringemulsioner inte tidigare undersökts för ultraljudsmedierade tillämpningar. Det är nödvändigt att känna till de mekaniska och akustiska egenskap, QC 2023-05-09

Bioelectronic medicine can treat diseases and disorders in humans by electrically interfacing with peripheral nerves. Multielectrode cuffs can be used for selective stimulation of portions of the nerve, which is advantageous for treatment specificity. The biocompatibility and conformability of cuffs can be improved by reducing the mechanical mismatch between nerve tissue and cuffs, but selective stimulation of nerves has yet to be achieved with soft and stretchable cuff electrodes. Here, this paper reports the development of a soft and stretchable multielectrode cuff (sMEC) for selective nerve stimulation. The device is made of 50 mu m thick silicone with embedded gold nanowire conductors, which renders it functional at 50% strain, and provides superior conformability for wrapping nerves. By using different stimulation protocols, high functional selectivity is achieved with the sMECs eight stimulation electrodes in a porcine sciatic nerve model. Finite element modeling is used to predict the potential distribution within the nerve, which correlate well with the achieved stimulation results. Recent studies are showing that mechanical softness is of outermost importance for reducing foreign body response. It is therefore believed that the soft high-performance sMEC technology is ideal for future selective peripheral nerve interfaces for bioelectronic medicine., Funding Agencies|Swedish Foundation for Strategic Research; Swedish Research Council [2019-04424]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoeping University (Faculty Grant SFO Mat LiU) [2009 00971]; European Research Council, "e-NeuroPharma" ERC-2018-ADG [834677]

Vårdens digitala system präglas idag av en hög komplexitet och saknar ensammanhållen standard. Ett ökat behov för digitalisering inom vården harlett till stora utvecklingar, men även nya utmaningar. Ett av de viktigastesystemen, som genomsyrar hela vårdsystemet, är patientjournalen. I detta arbete undersöks möjligheterna till att applicera en ny teknologi föranvändning inom den svenska vårdens informationshantering, blockkedjor. Blockkedjor har huvudsakligen använts för implementering avkryptovalutor men har, på grund av sina riktighet- och spårbarhetsgarantier,blivit alltmer vanlig för användning inom övrig informationshantering. Inledningsvis undersöks förutsättningarna som ett journalsystem ska levaupp till enligt säkerhetsprinciperna Konfidentialitet, Riktighet,Tillgänglighet och Spårbarhet. Intervjuer med områdeskunniga förjournalsystemen inom Region Halland och Region Stockholm belyser någraav de brister och utmaningar som finns inom vården idag. Informationsinhämtning från lagrum och föreskrifter lägger grunden till dekrav som ett journalsystem ska uppfylla. Efter den inledande undersökningen studeras litteratur kopplat tillimplementeringar av blockkedjor i syfte att hämta in information gällandeblockkedjans komponenter för vidare behandling. Här behandlas blandannat utvecklardokument, state-of-the-art och analyser relaterade tillblockkedjor. Slutligen bearbetas blockkedjetekniken ihop med journalsystemensförutsättningar och resulterar i en teoretisk modell av ett blockkedjesystemför användning som patientjournal inom den svenska vården. Slutsatsen dras att möjligheten för en praktisk implementering av denteoretiska modellen är möjlig, men att vården i dagsläget inte harförutsättningarna för ett omfattande systembyte till en relativt obeprövadteknologi.

På substratenheten på Karolinska Universitetssjukhuset inventerades och produktionsplanerades allt för hand på papper 3 gånger i veckan. Syftet med projektet har varit att hitta en digital lösning för att effektivisera arbetet kopplat till inventeringen och produktionsplaneringen. Målet har därför varit att utveckla en skrivbordsapplikation för att uppnå detta syfte genom avläsning av QR-koder. Projektet genomfördes genom programmering i språket Java och utnyttjade JavaFX för alla grafiska komponenter. Databaskopplingen som krävdes använde mySQL för att sammankoppla program med databas. För utvecklandet av applikationen så har designmönstret Model-View-Controller använts. Vi som författare anser att applikationen bör bearbetas vidare, detta då en del funktionalitet skulle behöva optimeras och även utvecklas vidare för att uppnå önskad användarvänlighet. Dock är vår slutsats att alla de uppsatta målen uppfylldes., At the substrate unit at Karolinska University Hospital they previously did their inventory and production planning by hand three times a week. The purpose of the project has been to find a digital solution to streamline this unit’s workload connected to inventory and production planning. Therefore, the aim of the project was to develop a desktop application to fulfill the purpose of the project by scanning QR-codes. The project has been performed through programming in the language Java and used the JavaFX library for all graphical components. The database connection used mySQL to connect the program to the database. For the development of the application the design-pattern Model-View-Controller were used. As authors of the report, we consider that the application should be further optimized, and additional functions should be introduced in order to achieve greater usability. However, our conclusion is that the application fulfills the aims set at the beginning of the project.

Cancer is a tough disease and in some cases is difficult to treat, which leads to too many people dying. Of the cancer diagnoses made each year, 3.2 million of these are primary and secondary liver cancer. The world needs to be able to reduce these tragic outcomes and to be able to remove a liver tumor in a more patient-centered way. Today, resection of the liver is performed through an open operation, which entails a long and more painful recovery process for the patient compared to a keyhole surgery. The reason keyhole surgery is not used in these operations today is because the surgeon lacks sufficient visualization, which makes it difficult to navigate and remove the tumor with high precision. Navari Surgical AB is therefore developing a solution that makes it possible to apply keyhole operations for liver cancer by increasing the visualization with Augmented Reality. For the technology to work, a single-use device must be attached to the liver surface and it then acts as a navigation marker in their overall solution. The mission has been to examine and develop a fastener for the single-use device that attaches to the liver and can be removed without damaging the surface. The fastener must also be able to maintain the same position throughout the operation, withstand the moist environment inside the abdomen and comply with the Medical Device Regulations. After research and several tests, the project was able to result in a working fastener, which is a specific hydrogel. Due to the fact that the hydrogel does not adhere to plastic, it will be attached to the single-use device with medical epoxy and a layer of carbon fiber film. Together, these components make up a complete and functioning product, called Navmark. Applying keyhole surgery for liver cancer with Navmark and Navari's technology has several advantages. First, together they increase the surgeon's visualization and localization of the tumor, which in turn increases the precision of the operation. Under

Böjsenor är senor i fingrarna som är kopplade till underarmens muskler. De läker långsamt efter en skada och kan därför gå av efter en reparation om patienten tränar för mycket eller för tungt. Vid för lite träning fastnar senan i omgivande vävnad och fingret blir stelt. Rehabiliteringen efter en böjsenskada pågår under minst tre månader efter operationen. Det kan därför bli svårt för patienter att upprätthålla motivationen och följa alla instruktioner korrekt under hela denna tid. Syftet med projektet var att skapa en mobilapplikation som stödjer patienter i deras rehabilitering av en böjsenskada samt att implementera ett digitalt vinkelmätningsverktyg i applikationen. Applikationen utvecklades för både iOS- och Android-enheter med hjälp av deras officiella utvecklingsmiljöer, Android Studio och Xcode. Några funktioner som applikationen består av är en kalender med översikt av träningsplanen, möjligheten att utvärdera pass, möjligheten att skapa träningsprogram och exportera träningsdata. Applikationen innehåller även information om träningsinnehållet, kontaktinformation och allmänna råd till patienten. Innan applikationen kan publiceras måste den testas på patienter för att få återkoppling om användarvänlighet och funktionalitet. De önskade funktionerna från uppdragsgivaren och målen uppnåddes med undantag av funktionen att lägga till möten som prioriterades bort. Det digitala vinkelmätningsverktyget implementerades för Android men inte för iOS., Flexor tendons are tendons in the fingers that are connected to the muscles in the forearm. They heal slowly after an injury and can rupture again after the repair if the patient exercises too much or lifts too heavy. Insufficient exercise can cause the tendon to adhere to surrounding tissue, resulting in finger stiffness. Rehabilitation following a flexor tendon injury typically lasts for at least three months after surgery. Therefore, patients may find it challenging to maintain motivation and follow all instructions correctly throughout this period. The purpose of this project was to develop a mobile application that supports patients in their rehabilitation of a flexor tendon injury along with the implementation of a digital angle measuring tool. The application was developed for both iOS and Android devices using their official integrated development environments, Android Studio and Xcode. Some features that the application consists of are a calendar providing an overview of the training plan, the ability to evaluate sessions, the ability to create custom training programs and export training data. Additionally, the application contains information about the training content, contact details, and general advice for the patient. Before the application can be published, it needs to be tested on patients to gather feedback on user-friendliness and functionality. The desired features from the client and the goals for the project were achieved, apart from the meeting scheduling feature, which was deprioritized. The digital angle measuring tool was implemented for Android but not for iOS.

Purpose - This study aims to provide an understanding of co-opetition as a strategy when medical technology companies collectively develop interoperability solutions for hospitals. Method - The study is an explorative multiple-case study involving medical technology companies, hospitals, and industry experts. A total of 20 interviews were conducted with respondents located in Sweden, the United States, and Italy in three waves. The analysis was conducted through a thematic analysis. Findings - The study resulted in an overview of two important phases to consider when engaging in co-opetition to achieve faultless interoperability. Namely the development and operational phase. Moreover, critical activities in the respective phases were illustrated in a process framework that also showcased interdependencies between activities. Theoretical contribution – This study extends the notion that co-opetition primarily revolves around developing new products and services and emphasizes that the operational phase of developed solutions is important to consider. Furthermore, it contributes to the literature by displaying the need to contextualize co-opetition with industry-specific considerations. Managerial implications – This study provides an overview for vendors on how to approach co-opetition to achieve faultless interoperability. Moreover, it shows that co-opetition is a deliberate strategy that needs to be evaluated if it should be pursued or not. Finally, managers need to have an awareness that co-opetition is a strategy to gain strategic benefits over other participants. Limitations and future research - The primary focus of this study has been on co-opetition among large companies in medical technology. This limits generalizability and future research should consider smaller companies in medical technology. Additionally, future research should consider hospitals’ perspective on interoperability and its implications on co-opetition in this context., Syfte – Denna studie avser att bidra med en förståelse kring ”co-opetition” som en strategi när medicintekniska företag tillsammans utvecklar interoperabilitetslösningar mot sjukhus. Metod – Denna studie är en explorativ multipel fallstudie som involverar företag inom medicinteknik, sjukhus och industriexperter. Totalt 20 intervjuer utfördes med respondenter som befann sig i Sverige, USA och Italien i tre omgångar. Den efterföljande analysen utfördes genom en tematisk analys. Resultat – Studien resulterade i en överblick över två viktiga faser att beakta för att uppnå felfri interoperabilitet. Nämligen utvecklingsfasen och den operativa fasen. Vidare illustrerades kritiska aktiviteter i respektive fas samt deras relation till varandra. Teoretiska bidrag – Denna studie utvidgar tron om att co-opetition huvudsakligen kretsar kring att utveckla nya produkter och tjänster, och betonar att den operativa fasen av utvecklade lösningar är viktig att beakta. Vidare bidrar studien till litteraturen genom att påvisa behovet av att sätta co-opetition i kontext med industrispecifika överväganden. Praktiska bidrag – Denna studie bidrar med en överblick för medicintekniska företag kring hur de ska bedriva co-opetition för att uppnå felfri interoperabilitet. Vidare gör studien anspråk på att co-opetition är en medveten strategi som måste utvärderas om den ska bedrivas eller inte. Slutligen måste företag vara medvetna om att co-opetition även fungerar som en strategi för att få strategiska fördelar över andra medverkande parter. Begränsningar och framtida forskning – Studien har huvudsakligen fokuserat på co-opetition bland stora medicintekniska företag. Detta begränsar generaliserbarheten och framtida forskning borde överväga mindre medicintekniska företag. Framtida forskning borde dessutom överväga sjukhusens perspektiv på interoperabilitet och dess implikationer på co-opetition i detta sammanhang.

Cardiovascular diseases are the most common cause of death worldwide. There are known risk factorsfor cardiovascular diseases, such as high blood pressure, and it is also known that cardiovasculardiseases are associated with both the macrocirculation and the microcirculation. The macrocirculationis the term for the large vessels within the circulatory system, while microcirculation refersto the small vessels near the cells. In the clinics, the macrocirculation is more commonly examinedthan the microcirculation. Hypothetically indications of cardiovascular diseases could be foundearlier in the microcirculation than in the macrocirculation. If that is true, cardiovascular diseasescould be found earlier than as of today by microcirculatory examinations. The microcirculation hasbeen studied previous with mathematical models. However, there is no mathematical model on theoxygen transport in the skin microcirculation that constitute ordinary differential equations, has ahigh time resolution and where the impacts on the oxygen transport of risk factors of cardiovasculardiseases are studied.This thesis showcases that the microvascular oxygen transport is possible to study in high resolutionwith a mathematical model and that high blood pressure may have an impact on the abilityto recruit more vessels due to a perturbation of the blood system. The model shows the main principlesof the dynamics of the oxygen in skin microcirculation. Although the model is a simplificationof the real microcirculatory system, the model is able to describe experimental data. Further knowledgeabout the microcirculation and the impact of blood pressure has been drawn from the model.An improved model could be used to understand and learn even more about the microcirculationand the impact of more risk factors besides high blood pressure. That knowledge is important tofind and treat cardiovascular diseases earlier than as of today to worldwide decrease the morbidityand mortality of cardiovascular dise

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Mellan år 2020 och år 2021 registrerades 381 operationer av böjsenskador i Sverige. Rehabiliteringsprocessen efter en böjsenskada är lång och kräver kontinuerlig uppföljning. Uppföljningen sker genom mätningar av böj- och sträckförmågan i de fingrar som patienten opererat. Mätningarna utförs i nuläget manuellt med goniometer, där vinklarna i fingrarnas leder mäts, en gång per vecka på en handkirurgisk mottagning. Handkirurgiska kliniken på Södersjukhuset i Stockholm efterfrågade ett digitalt mätverktyg som skulle göra det möjligt för patienten att på ett tillförlitligt sätt mäta vinklarna hemma i stället för på kliniken. Målet med projektet var således att skapa ett mätverktyg som mäter vinklarna med ett mätfel på högst 5 grader. Ett mätverktyg utvecklades som utför mätning av vinklar med hjälp av en bild användaren tagit. Mätverktyget är kompatibelt med Androidenheter och kan implementeras i en redan existerande Androidapplikation. Tester visade att mätverktyget har ett mätfel med ett medelvärde på 5 grader eller lägre för proximala interfalangealleden och distala interfalangealleden. Vidare var medelfelet 7,3 grader respektive 6,4 grader för metakarpofalangealleden vid böjt respektive sträckt finger, samt 10 grader för totalt aktivt rörelseomfång (total active motion)., Between 2020 and 2021, 381 operations for flexor tendon injuries were registered in Sweden. The rehabilitation following surgery is long and requires continuous monitoring. Presently the monitoring is done through manual measurements of the patients flexing and extending capability of the fingers affected. The measurement is performed at a hand surgery department with a handheld goniometer, initially once a week. The hand surgery department at Södersjukhuset in Stockholm requested a digital tool that allows the patient to reliably measure the angles of the fingers bending and stretching capability at home instead of at the clinic. The objective of the project was therefore to create a measurement tool that measures these angles with a maximum error of 5 degrees. A measurement tool was developed where the measurement of angles takes place from an image taken by the user. The measurement tool is compatible with Android devices and can be implemented in an existing Android application. Tests showed that the measurement tool has a mean measurement error of 5 degrees or less for the proximal interphalangeal joint and the distal interphalangeal joint. In addition, the mean error was 7.3 degrees and 6.4 degrees for the metacarpophalangeal joint, bent and stretched respectively, and 10 degrees for total active motion.

The General Data Protection Regulation has undoubtedly affected our society, both on anindividual everyday level as well as from the greater perspective of companies, the publicsector, and nations. The purpose of the GDPR is to protect the data of European citizens byputting further responsibility on organizations that store individual data. However, as withevery decision, this has had implications that might not have been predicted or accountedfor and which can disrupt its initial cause. Certain industries have been highly regulatedwhen it comes to data even before the GDPR, one of these is the health-tech industry whichmanages medical data which is perceived to be very sensitive and has for example beenregulated through the Patient Data Act.There is currently a research gap regarding how the GDPR has affected organizations andtheir journey toward compliance. This qualitative study was conducted using a criticalrealism perspective with a critical constructivist approach. The study is done incollaboration with the Swedish Kubernetes platform service provider Elastisys. Byconducting interviews with both the company itself and also with some of their health-techclients, as well as looking into cases where healthcare organizations have been fined underGDPR this thesis aimed towards answering the question of, “What effects have the GDPRlegislation had on the health-tech market and how have the organizations within itadapted?”.The result of this thesis show that the organizations have been able to reach compliance andmanage the process, however the implications have been clearer and more understandableas time has passed. This could be partly explained by the growing number of support-toolsand -organizations available today. Furthermore, due to the strong regulations and changingconditions within this particular industry the organizations are used to rapid transitions.Something that has implicated the general digital development of the industry but also madethem more adap

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.