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Every year, the Food and Drug Administration (FDA) clears approximately 3,000 medical devices for marketing via the 510(k) pathway. These constitute 99% of all devices approved for human use and includes the premarket review of many devices incorporating newer technology such as artificial intelligence (AI), machine learning (ML), and other software. As the complexity of these novel technologies and the number of applications is expected to increase in the coming years, statutory changes such as the 2016 21st Century Cures Act, regulations, and guidance documents have increased both the volume and complexity of device review. Thus, the ability to streamline the review of less complex, low-to-moderate risk devices through the 510(k) pathway will maximize the FDA's capability to address other important, future-oriented regulatory questions. For over twenty five years, third party review organizations have served a defined function to assist with the review of 510(k) applications for a set of enumerated device classes. This paper reviews the history of FDA device regulation, the evolution of the 510(k) review pathway, and the recent history of the 510(k) third party review program. Finally, the paper addresses policy concerns from all stakeholders – including the FDA – along with policy suggestions to improve the third party review program and FDA device regulation writ large. [ABSTRACT FROM AUTHOR]

Usability plays an important role in eHealth applications for their widespread adoption. These software systems have been studied in depth in the literature from this perspective. However, the energy consumption of information systems in the eHealth domain lacks comprehensive research. In this paper, the relationship between the energy consumption of the main components of a PC and the usability evaluations of graphical user interfaces (GUI) were studied in client applications. For this purpose, personal health records (PHR) were used as a case study. A set of 4 web-based PHRs were evaluated with the performance of 20 common tasks in the aforementioned systems. The usability evaluations were carried out by 4 experts. A total of 8 heuristics particularly designed for electronic health record systems and based on the Nielsen usability heuristics were employed. The instantaneous energy values of the power supply and the monitor were collected with an energy consumption measuring equipment. As a result, the following significant correlations were found. In NoMoreClipboard between the Memory heuristic and the power supply (rs = 0.495, p = 0.044), and the Minimalist heuristic and the monitor (rs = − 0.513, p = 0.035). In PatientsLikeMe between the Minimalist heuristic and the power supply (rs = − 0.479, p = 0.083), as well as in the Error heuristic and the power supply (rs = − 0.638, p = 0.014). Finally, in Health Companion between the Match heuristic and the power supply (rs = − 0.481, p = 0.043), and the Error heuristic and the power supply again (rs = − 0.602, p = 0.008). Apart from that, the tasks were divided into those with excellent and fair usability. Those tasks with high usability scores were evaluated to detect whether they also had low energy consumption during task performance. Significant differences were found in PatientsLikeMe with respect to the Error heuristic and the power supply (212.39 ± 18.85W). Significant differences were also found in Health Companion with respect to the Match heuristic and the power supply (199.91 ± 7.19W). The results were discussed to reveal the fundamentals of how implementing usability can impact on energy costs when running client applications. [ABSTRACT FROM AUTHOR]

Due to the development of modern wearable mobile devices, the need of antenna with smaller size and internally flexible to fit becomes necessary. Miniaturization of Micro Strip Patch (MSP) antenna increases its employability for communication in different aspects. The use of flexible material for the fabrication of MSP antenna still improves its use for Wireless Body Area Networks (WBAN) which includes devices for monitoring systems in military, surveillance and medical applications. The devices designed specifically in Industrial Scientific Medical (ISM) band are used for communication in these applications. Defected Ground Structure (DGS) is adopted as an emerging technique for improving the various parameters of microwave circuits, that is, narrow bandwidth, cross-polarization, low gain, and so forth. In this paper, the design of compact micro strip patch antenna using different flexible substrate materials with DGS is proposed to resonate the antenna at 2.45GHz ISM band which can be used as biomedical sensors. Felt and Teflon with dielectric constant 1.36 and 2.1respectively are chosen as flexible substrate material among various flexible materials like cotton, rubber, paper, jeans etc. Using CST studio suite software, the designed antenna is simulated and the fabricated antenna is tested with Vector Network Analyzer (VNA). The performance parameters like return loss, gain, directivity and Voltage Standing Wave Ratio (VSWR) of the antenna are analyzed. [ABSTRACT FROM AUTHOR]

Employing software engineering to build an integrated, standardized, and scalable solution is closely associated with the healthcare domain. Furthermore, new diagnostic techniques have been developed to obtain better results in less time, saving costs, and bringing services closer to the most unprotected areas. This paper presents the integration of a top-notch component, such as hardware, software, telecommunications, and medical equipment, to produce a complete system of Electronic Health Record (EHR). The EHR implementation aims to contribute to the expansion of the health services offer concerning people who live in locations where typically have difficult access to medical care. The methodology throughout the work is a Strategic Planning to set priorities, focus energy and resources, strengthen operations, ensure that directors, managers, employees, and other stakeholders are working toward common goals, establish agreement around intended outcomes/results. A medical and technical team is incorporated to complete the tasks of process and requirements analysis, software coding and design, technical support, training, and coaching for EHR system users throughout the implementation process. The adoption of those tools reflect notably some expected results and benefits on patient care. The EHR implementation ensures that information collection does not duplicate already existing information or duplicate effort and maximize the practical use of the data collected. Moreover, the EHR reduces mistakes in hospital readmissions, improves paperwork, promotes the progress of the state's health care system providing emergency, specialty, and primary health care in a rural area of Campeche. The EHR implementation is critical to support decision making and to promote public health. The total number of consults increased markedly from 2012 (14021) to 2019 (34751). The most commonly treated diseases in this region of Mexico are hypertension (17632) and diabetes (13156). The best results are obtained in the Nutrition (20,61%) and clinical psychology services (16,67%), and the worst levels are registered in pediatric and surgical oncology services where only 1,59% and 1,97% of the patients are admitted in less than 30 min, respectively. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) is being integrated into many different areas of our lives, from industrial engineering to video-games, and also including teaching and education. We have several examples where VR has been used to engage students and facilitate their 3D spatial understanding, but can VR help also teachers? What are the benefits teachers can obtain on using VR applications? In this paper we present an application (VR4Health) designed to allow students to directly inspect 3D models of several human organs by using Virtual Reality systems. The application is designed to be used in an HMD device autonomously as a self-learning tool and also reports information to teachers in order that he/she becomes aware of what the students do and can redirect his/her work to the concrete necessities of the student. We evaluate both the students' and the teachers' perception by doing an experiment and asking them to fill-in a questionnaire at the end of the experiment. [ABSTRACT FROM AUTHOR]

Historically, medical imaging repositories have been supported by indoor infrastructures. However, the amount of diagnostic imaging procedures has continuously increased over the last decades, imposing several challenges associated with the storage volume, data redundancy and availability. Cloud platforms are focused on delivering hardware and software services over the Internet, becoming an appealing solution for repository outsourcing. Although this option may bring financial and technological benefits, it also presents new challenges. In medical imaging scenarios, communication latency is a critical issue that still hinders the adoption of this paradigm. This paper proposes an intelligent Cloud storage gateway that optimizes data access times. This is achieved through a new cache architecture that combines static rules and pattern recognition for eviction and prefetching. The evaluation results, obtained from experiments over a real-world dataset, show that cache hit ratios can reach around 80%, leading to reductions of image retrieval times by over 60%. The combined use of eviction and prefetching policies proposed can significantly reduce communication latency, even when using a small cache in comparison to the total size of the repository. Apart from the performance gains, the proposed system is capable of adjusting to specific workflows of different institutions. [ABSTRACT FROM AUTHOR]

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated. [ABSTRACT FROM AUTHOR]

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community. [ABSTRACT FROM AUTHOR]

This paper presents a software system that visualizes the impact of the master surgery schedule on the demand for various resources throughout the rest of the hospital. The master surgery schedule can be seen as the engine that drives the hospital. Therefore, it is very important for decision makers to have a clear image on how the demand for resources is linked to the surgery schedule. The software presented in this paper enables schedulers to instantaneously view the impact of, e.g., an exchange of two block assignments in the master surgery schedule on the expected resource consumption pattern. A case study entailing a large Belgian surgery unit illustrates how the software can be used to assist in building better surgery schedules. [ABSTRACT FROM AUTHOR]

This paper presents preliminary results from a power quality audit conducted at a clinical building over the last year. Voltage and current were measured at various laboratories; it was found that the main problems for the equipment installed were voltage sags and surges. The paper examines the causes and effects of power disturbances that affect computer or any other microprocessor-based equipment and analyzes the auto-protection capabilities of modern power supplies. The convenience of “enhanced power supply” or “low-cost customer-side” protection solutions is also discussed. Finally it is addresses the role of the Standards on the protection of electronic equipment and the implications for the final costumer. [ABSTRACT FROM AUTHOR]

In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process. [ABSTRACT FROM AUTHOR]

Surgical navigation systems can help doctors maximize the accuracy of surgeries, minimize operation durations, avoid mistakes, and improve the survival chances of patients. The tracking of device is an important component in surgical navigation systems. However, commercial surgical tracking devices are expensive, thus hindering the development of surgical navigation systems, particularly in developing countries. Therefore, an accurate and low-cost near-infrared optical tracking system is presented in this study for the real-time tracking of surgical tools and for measuring and displaying the positions of these tools relative to lesions and other targets inside a patient's body. A relative algorithm for the registration of surgical tools is also proposed in this paper to yield easy, safe, and precise tracking. Experiments are conducted to test the performance of the system. Results show that the mean square errors of the distances between the light-emitting points on the surgical tools are less than 0.3 mm, with the mean square error of distance between the tip and light-emitting points is less than 0.025 mm and that between two adjacent corner points is 0.2714 mm. [ABSTRACT FROM AUTHOR]

This paper investigates how a healthcare network in Taiwan uses a practical cloud image data center (CIDC) to communicate with its constituent hospital branches. A case study approach was used. The study was carried out in the central region of Taiwan, with four hospitals belonging to the Veterans Hospital healthcare network. The CIDC provides synchronous and asynchronous consultation among these branches. It provides storage, platforms, and services on demand to the hospitals. Any branch-client can pull up the patient's medical images from any hospital off this cloud. Patients can be examined at the branches, and the images and reports can be further evaluated by physicians in the main Taichung Veterans General Hospital (TVGH) to enhance the usage and efficiency of equipment in the various branches, thereby shortening the waiting time of patients. The performance of the CIDC over 5 years shows: (1) the total number of cross-hospital images accessed with CDC in the branches was 132,712; and (2) TVGH assisted the branches in keying in image reports using the CIDC 4,424 times; and (3) Implementation of the system has improved management, efficiency, speed and quality of care. Therefore, the results lead to the recommendation of continuing and expanding the cloud computing architecture to improve information sharing among branches in the healthcare network. [ABSTRACT FROM AUTHOR]

Stakeholders' needs and expectations are identified by means of software quality requirements, which have an impact on software product quality. In this paper, we present a set of requirements for mobile personal health records (mPHRs) for pregnancy monitoring, which have been extracted from literature and existing mobile apps on the market. We also use the ISO/IEC 25030 standard to suggest the requirements that should be considered during the quality evaluation of these mPHRs. We then go on to design a checklist in which we contrast the mPHRs for pregnancy monitoring requirements with software product quality characteristics and sub-characteristics in order to calculate the impact of these requirements on software product quality, using the ISO/IEC 25010 software product quality standard. The results obtained show that the requirements related to the user's actions and the app's features have the most impact on the external sub-characteristics of the software product quality model. The only sub-characteristic affected by all the requirements is Appropriateness of Functional suitability. The characteristic Operability is affected by 95 % of the requirements while the lowest degrees of impact were identified for Compatibility (15 %) and Transferability (6 %). Lastly, the degrees of the impact of the mPHRs for pregnancy monitoring requirements are discussed in order to provide appropriate recommendations for the developers and stakeholders of mPHRs for pregnancy monitoring. [ABSTRACT FROM AUTHOR]

Patient medication safety is an important issue in patient medication systems. In order to prevent medication errors, integrating Radio Frequency Identification (RFID) technology into automated patient medication systems is required in hospitals. Based on RFID technology, such systems can provide medical evidence for patients' prescriptions and medicine doses, etc. Due to the mutual authentication between the medication server and the tag, RFID authentication scheme is the best choice for automated patient medication systems. In this paper, we present a RFID mutual authentication scheme based on elliptic curve cryptography (ECC) to enhance patient medication safety. Our scheme can achieve security requirements and overcome various attacks existing in other schemes. In addition, our scheme has better performance in terms of computational cost and communication overhead. Therefore, the proposed scheme is well suitable for patient medication systems. [ABSTRACT FROM AUTHOR]

Accurately and efficiently identifying the location of patients during the course of rehabilitation is an important issue. Wireless transmission technology can reach this goal. Tracking technologies such as RFID (Radio frequency identification) can support process improvement and improve efficiencies of rehabilitation. There are few published models or methods to solve the problem of positioning and apply this technology in the rehabilitation center. We propose a mechanism to enhance the accuracy of positioning technology and provide information about turns and obstacles on the path; and user-centered services based on location-aware to enhanced quality care in rehabilitation environment. This paper outlines the requirements and the role of RFID in assisting rehabilitation environment. A prototype RFID hospital support tool is established. It is designed to provide assistance for monitoring rehabilitation patients. It can simultaneously calculate the rehabilitant's location and the duration of treatment, and automatically record the rehabilitation course of the rehabilitant, so as to improve the management efficiency of the rehabilitation program. [ABSTRACT FROM AUTHOR]

The accuracy, validity and lack of relation information between dental root and jaw in tooth arrangement are key problems in tooth arrangement technology. This paper aims to describe a newly developed virtual, personalized and accurate tooth arrangement system based on complete information about dental root and skull. Firstly, a feature constraint database of a 3D teeth model is established. Secondly, for computed simulation of tooth movement, the reference planes and lines are defined by the anatomical reference points. The matching mathematical model of teeth pattern and the principle of the specific pose transformation of rigid body are fully utilized. The relation of position between dental root and alveolar bone is considered during the design process. Finally, the relative pose relationships among various teeth are optimized using the object mover, and a personalized therapeutic schedule is formulated. Experimental results show that the virtual tooth arrangement system can arrange abnormal teeth very well and is sufficiently flexible. The relation of position between root and jaw is favorable. This newly developed system is characterized by high-speed processing and quantitative evaluation of the amount of 3D movement of an individual tooth. [ABSTRACT FROM AUTHOR]

Telecare medical information systems (TMIS), with the explosive growth of communication technology and physiological monitoring devices, are applied increasingly to enable and support healthcare delivery services. In order to safeguard patients' privacy and tackle the illegal access, authentication schemes for TMIS have been investigated and designed by many researchers. Many of them are promising for adoption in practice, nevertheless, they still have security flaws. In this paper, we propose a novel remote authentication scheme for TMIS using self-certified public keys, which is formally secure in the ID-mBJM model. Besides, the proposed scheme has better computational efficiency. Compared to the related schemes, our protocol is more practical for telemedicine system. [ABSTRACT FROM AUTHOR]

The article presents a study which described an electronic graphically based source documentation technology that maintains and mimics the advantages of paper recordkeeping. The technology complies with the requirements for electronic systems issued by the U.S. Food and Drug Administration (FDA) like backup of data, safeguards on digital signatures, and validation of all system components. Also cited are the components of the technology like a main file server and tablet computers.

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with LabVIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and pre-processing, time–frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time–frequency/scale spectral analysis techniques such as STFT, Wigner–Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI = 5–15 and 14 subjects), middle OSAS (AHI < 15–30 and 18 subjects), and heavy OSAS (AHI > 30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50–50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome. [ABSTRACT FROM AUTHOR]

This paper desires to explore the role of barcode technology in blood bank information systems via addressing its upgrade and the consequent impact. Firstly, it briefs the application and effect of barcode technology in blood donation and transfusion service. In the second, the barcode paradigms of Macau Codabar and ISBT 128 are carefully examined and compared in accordance with the procedure and specifications of barcode upgrade. Then, the impact due to upgrade is assessed from the perspectives of blood bank information system, blood banks, and even blood donation and transfusion service. It finally discusses the considerations on implementing the upgrade of barcode technology in blood bank information systems. [ABSTRACT FROM AUTHOR]

Software reliability analysis is inevitable for modern medical systems, since a large amount of medical system functionality is now dependent on software, and software does contribute to system failures. Most software reliability models are based on software failure data collected from the project. This creates a problem for the designers since, during the early stage, software failure data are not available. However, a valuable knowledge can be learned from the analysis of previous projects and applied to the new ones. This paper presents the approach that predicts the potentially dangerous software modules under development based on the analysis of the already finished modules using the machine-learning techniques. On the basis of the prediction given by our method software designers are able to devote more testing effort to the dangerous parts of the system, which results in a more reliable medical software system. [ABSTRACT FROM AUTHOR]

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE. [ABSTRACT FROM AUTHOR]

Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management. [ABSTRACT FROM AUTHOR]

Nowadays, one of the choice techniques for the spasticity treatment is the ultrasound-guided infiltration of Botulinum Toxin, because it is safe and effective. In order to medical professionals can carry out this technique, they need training and education. One of the safest and most time-free ways to facilitate the acquisition of practical medical skills is through simulators. In this paper we present an innovative technological environment, which includes an ultrasound simulator for training in muscle exploration and infiltration. The simulation platform will guide health professionals, with great realism and high degree of interactivity, in the autonomous training of all the tasks involved in the spasticity treatment procedure by infiltration of Botulinum Toxin, without the need for a real patient or costly phantoms. [ABSTRACT FROM AUTHOR]

Dialogue-based simulation is a real-world practice technique for medical and clinical education that provides students with an opportunity to train using hands-on experiences without putting actual patients being put at risk. In this paper, a 3D interactive dialogue-based training and assessment system that supports the detailed development of clinical trial competency for medical students in a distributed virtual environment was proposed. For clinical training, MCRDR-based natural language understanding to realize the semantic representation of written dialog from the most relevant inference results was applied, and on the basis of this, a convolutional neural network model was also used to make the generated inference more exact and reliable. For clinical assessment, the dialogue-driven competency method was used to encompass medical knowledge, communication skill as well as professionalism skill based on the collected dialogue information. Finally, the potential of the created system was demonstrated with several clinical cases. The preliminary results indicate that the system demonstrates the potential of providing efficient training and flexible assessment, while saving time, improving practical skills and making students more confident. [ABSTRACT FROM AUTHOR]

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance. [ABSTRACT FROM AUTHOR]

The European Union has a substantial investment in research and development and demand side-measures in the health sector in order to promote new initiatives, prevent disease and foster healthy lifestyles. In particular, the European Commission and other European entities have funded research projects focused on the use of technology in the health sector. In this context, health research initiatives have evolved from user-centred monolithic solutions into collaborative partnerships of different stakeholders that gather around different technological platforms. In order to identify the lacks and opportunities in this area, a systematic mapping study was conducted with the aim of identifying and analysing the recent research projects developed in Europe related to technological ecosystems in the health sector. The study covered closed European research projects from 2003 to 2018. This paper aims to extend that systematic mapping study through ongoing research projects. The analysis of these research projects provides an overview of the current trends and identify the lacks and opportunities to define new advances in this research area. Moreover, the comparison between the first mapping study focused on closed projects, and the current study, allows getting an overview of the evolution of technological ecosystems in the health sector. [ABSTRACT FROM AUTHOR]

In AES, the total time taken by the architecture while implementing in low power and high-speed circuit is the most important thing that to be considered. Also in AES, implementing S-Boxes consumes the major part of the total time consumed by the entire architecture. In this research paper, we propose a very low-power and high efficient S-Box circuit architecture: a multi-stage modified version of PPRM architecture over composite fields. In this modified S box design, only AND and Hazard transparent XOR gates are used. Because of this architecture dynamic hazards which form the main aspect of power consumption in S box gets eliminated. A low propagation delay of 4.58 ns and occupies 120 slices in the xilinx FPGA device xc6vlx75t-3ff784, while the low propagation delay and slice area is 5.552 ns and 120 respectively for the conventional PPRM architecture. This new proposed architecture is used to protect the mammographic images from being unauthorized access. [ABSTRACT FROM AUTHOR]

Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy. [ABSTRACT FROM AUTHOR]

Electrogastrography (EGG) is a non-invasive examination method for investigating the myolectrical activity of a stomach. Nowadays, abdominal surface electrogastrography is the one of methods of stomach examination that is used for diagnosing patients with chronic intractable nausea, vomiting and gastroparesis. The electrogastrographic signals are recorded by using cutaneous electrodes placed on the stomach surface. EGG DWPack system is a highly developed and easy to use software package for four channel electrogastrography recording and analysis. The part of the software for analysis is a MATLAB based software and requires the specific ASCII format of the EGG data. The analyzed EGG signals could be conditioned with the wide range of sampling frequency and various resolutions of analog to digital conversion. Additionally, if the EGG data fulfills certain conditions associated with sampling frequency, the software can be used to study the basic parameters of heart rate variability (HRV) simultaneously with the EGG parameters. The software includes different digital filters for the EGG signal extraction and tools for artifacts exclusion. The software computes the majority of EGG parameters which are commonly used in a clinical practice. The EGG analysis can be made for several adjustable analysis settings and various methods, and it can optimize the analysis methods for different preferences or requirements. The analysis result can be saved in a MAT-file, and exported to MS Excel and ASCII files. Validation of the software was performed using synthetic and real EGG signals. This paper contains, as an example of use, an analysis of four synthetic, and fourteen human 4-channel EGG data recording with water, yogurt and a solid meal stimulation. The mean values of the dominant frequency for fast, and postprandial stage were found to be 2.96±0.21 cpm (cycle per minute), and 3.05±0.33 cpm, respectively. The values established in the validation process are consistent with typical human physiological values. In addition, the results were compared to outcomes from commercial system. The results of validation have proved that EGG DWPack software produces reliable outcomes. The software is available for free of charge for Windows operating system for the all possible non commercial use. [ABSTRACT FROM AUTHOR]

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances. [ABSTRACT FROM AUTHOR]

Health Level Seven (HL7) is one of the standards most used to centralize data from different vital sign monitoring systems. This solution significantly limits the data available for historical analysis, because it typically uses databases that are not effective in storing large volumes of data. In industry, a specific Big Data Historian, known as a Process Information Management System (PIMS), solves this problem. This work proposes the same solution to overcome the restriction on storing vital sign data. The PIMS needs a compatible communication standard to allow storing, and the one most commonly used is the OLE for Process Control (OPC). This paper presents a HL7-OPC Server that permits communication between vital sign monitoring systems with PIMS, thus allowing the storage of long historical series of vital signs. In addition, it carries out a review about local and cloud-based Big Medical Data researches, followed by an analysis of the PIMS in a Health IT Environment. Then it shows the architecture of HL7 and OPC Standards. Finally, it shows the HL7-OPC Server and a sequence of tests that proved its full operation and performance. [ABSTRACT FROM AUTHOR]

The huge increases in medical devices and clinical applications which generate enormous data have raised a big issue in managing, processing, and mining this massive amount of data. Indeed, traditional data warehousing frameworks can not be effective when managing the volume, variety, and velocity of current medical applications. As a result, several data warehouses face many issues over medical data and many challenges need to be addressed. New solutions have emerged and Hadoop is one of the best examples, it can be used to process these streams of medical data. However, without an efficient system design and architecture, these performances will not be significant and valuable for medical managers. In this paper, we provide a short review of the literature about research issues of traditional data warehouses and we present some important Hadoop-based data warehouses. In addition, a Hadoop-based architecture and a conceptual data model for designing medical Big Data warehouse are given. In our case study, we provide implementation detail of big data warehouse based on the proposed architecture and data model in the Apache Hadoop platform to ensure an optimal allocation of health resources. [ABSTRACT FROM AUTHOR]