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Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

Assessing the current Artificial intelligence (AI) situation is a crucial step towards its implementation into radiology practice. The study aimed to assess radiographer willingness to accept AI in radiology work practice and the impact of AI in work performance. An exploratory cross-sectional online survey conducted for radiographers working within the Middle East and India was conducted from May–August 2020. A previously validated survey used to obtain radiographer's demographics, knowledge, perceptions, organization readiness, and challenges of integrating AI into radiology. The survey was accessible for radiographers and distributed through the societies page. The survey was completed by 549 radiographers distributed as (77.6%, n = 426) from the Middle East while (22.4%, n = 123) from India. A majority (86%, n = 773) agreed that AI currently plays an important role in radiology and (88.0%, n = 483) expected that AI would play a role in radiology practice and image production. The challenges for AI implementation in practice were developing AI skills (42.8%, n = 235) and AI knowledge development (37.0%, n = 203). Participants showed high interest to integrate AI in under and postgraduate curriculum. There is excitement about what AI could offer, but education input is a requirement. Fears are expressed about job security and how radiology may work across all ages and educational backgrounds. Radiographers become aware of AI role and challenges, which can be improved by education and training.

For advanced tongue cancer, the choice between surgery and organ-sparing treatment is often dependent on the expected loss of tongue functionality after treatment. Biomechanical models might assist in this choice by simulating the post-treatment function loss. However, this function loss varies between patients and should, therefore, be predicted for each patient individually. In the present study, the goal was to better predict the postoperative range of motion (ROM) of the tongue by personalizing biomechanical models using diffusion-weighted MRI and constrained spherical deconvolution reconstructions of tongue muscle architecture. Diffusion-weighted MRI scans of ten healthy volunteers were obtained to reconstruct their tongue musculature, which were subsequently registered to a previously described population average or atlas. Using the displacement fields obtained from the registration, the segmented muscle fiber tracks from the atlas were morphed back to create personalized muscle fiber tracks. Finite element models were created from the fiber tracks of the atlas and those of the individual tongues. Via inverse simulation of a protruding, downward, left and right movement, the ROM of the tongue was predicted. This prediction was compared to the ROM measured with a 3D camera. It was demonstrated that biomechanical models with personalized muscles bundles are better in approaching the measured ROM than a generic model. However, to achieve this result a correction factor was needed to compensate for the small magnitude of motion of the model. Future versions of these models may have the potential to improve the estimation of function loss after treatment for advanced tongue cancer.

An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p

In recent years, the incidence of lung cancer has been increasing. Lung cancer detection is based on computed tomography (CT) imaging of the lung area to determine whether there are pulmonary nodules. And then judge what’s good and what’s bad. However, due to the traditional way of manual reading and lack of experience and other problems. This leads to visual fatigue and misdiagnosis and missed diagnosis. In order to detect pulmonary nodules early and accurately, a new assistant diagnosis method for pulmonary nodules is proposed. Firstly, the image is preprocessed and denoised by median filter, the lung parenchyma is segmented by random walk algorithm and the region of interest is extracted, and then, according to the continuity of the CT slices, the texture feature extraction method of pulmonary nodules based on volume local direction ternary pattern is used to extract the features. Finally, the pulmonary nodules are identified and classified by the assistant diagnosis model of pulmonary nodules based on Stacking algorithm. In order to illustrate the validity of the diagnosis model, the experiments are carried out by cross-validation of ten folds. Experiments using data from LIDC database show that the accuracy, sensitivity and specificity of the proposed method are 82.2%, 85.7%, and 78.8%, respectively. Texture Recognition method based on volume vocal direction ternary pattern is feasible for the identification of pulmonary nodules and provides a reference value for doctor-assisted diagnosis., GRAPHICAL ABSTRACT

Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA’s multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidisciplinary data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55 % classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model’s output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.

This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease -- carotid artery stenosis (CAS), subclavian artery stenosis (SAC), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA) -- are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, which is adapted from the authors previous work and augmented to include the four disease forms. Six ML methods -- Naive Bayes, Logistic Regression, Support Vector Machine, Multi-layer Perceptron, Random Forests, and Gradient Boosting -- are compared with respect to classification accuracies and it is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the F1 score and computation of sensitivities and specificities. When using all the six measurements, it is found that maximum F1 scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, it is found that the performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that F1 scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates, Changelog: Added a bullet point in the discussion (end of section 3.3)

The structure of the human musculo-skeletal systems shows complex passive dynamic properties, critical for adaptive grasping and motions. Through wrist and arm actuation, these passive dynamic properties can be exploited to achieve nuanced and diverse environment interactions. We have developed a passive anthropomorphic robot hand that shows complex passive dynamics. We require arm/wrist control with the ability to exploit these. Due to the soft hand structures and high degrees of freedom during passive-object interactions, bespoke generation of wrist trajectories is challenging. We propose a new approach, which takes existing wrist trajectories and adapts them to changes in the environment, through analysis and classification of the interactions. By analysing the interactions between the passive hand and object, the required wrist motions to achieve them can be mapped back to control of the hand. This allows the creation of trajectories which are parameterized by object size or task. This approach shows up to 86% improvement in grasping success rate with a passive hand for object size changes up to +/-50%., EPSRC Doctoral Training Programme Studentship and Arm Ltd (RG99055).

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Made available in DSpace on 2021-06-25T11:54:44Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-03-17 Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation. Sao Paulo State Univ Julio de Mesquita Filho, Med Sch, Aracatuba, Brazil Sao Paulo State Univ Julio de Mesquita Filho, Inst Biosci, Aracatuba, Brazil Sao Paulo State Univ Julio de Mesquita Filho, Med Sch, Aracatuba, Brazil Sao Paulo State Univ Julio de Mesquita Filho, Inst Biosci, Aracatuba, Brazil

© The Author(s) 2017. The influence of an aortic aneurysm on blood flow waveforms is well established, but how to exploit this link for diagnostic purposes still remains challenging. This work uses a combination of experimental and computational modelling to study how aneurysms of various size affect the waveforms. Experimental studies are carried out on fusiform-type aneurysm models, and a comparison of results with those from a one-dimensional fluid–structure interaction model shows close agreement. Further mathematical analysis of these results allows the definition of several indicators that characterize the impact of an aneurysm on waveforms. These indicators are then further studied in a computational model of a systemic blood flow network. This demonstrates the methods’ ability to detect the location and severity of an aortic aneurysm through the analysis of flow waveforms in clinically accessible locations. Therefore, the proposed methodology shows a high potential for non-invasive aneurysm detectors/monitors.

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms. Validerad;2017;Nivå 2;2017-12-05 (rokbeg)

Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients.

Purpose Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. Methods COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Results Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Conclusion Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets. Electronic supplementary material The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons.

This paper is the first in the two-part series quantitatively modelling human grasp functionality and understanding the way human grasp objects. The aim is to investigate the thumb movement behavior influenced by object shapes, sizes, and relative positions. Ten subjects were requested to grasp six objects ( 3 shapes × 2 sizes ) in 27 different relative positions ( 3 X deviation × 3 Y deviation × 3 Z deviation ). Thumb postures were investigated to each specific joint. The relative position ( X , Y , and Z deviation) significantly affects thumb opposition rotation (Rot) and flexion (interphalangeal (IP) and metacarpo-phalangeal (MCP)), while the object property (object shape and size) significantly affects thumb abduction/adduction (ABD) motion. Based on the F value, the Y deviation has the primary effects on thumb motion. When the Y deviation changing from proximal to distal, thumb opposition rotation (Rot) and flexion (IP and MCP joint) angles were increased and decreased, respectively. For principal component analysis (PCA) results, thumb grasp behavior can be accurately reconstructed by first two principal components (PCs) which variance explanation ratio reached 93.8% and described by the inverse and homodromous coordination movement between thumb opposition and IP flexion. This paper provides a more comprehensive understanding of thumb grasp behavior. The postural synergies can reproduce the anthropomorphic motion, reduce the robot hardware, and control dimensionality. All of these provide a more accurate and general basis for the design and control of the bionic thumb and novel wearable assistant robot, thumb function assessment, and rehabilitation.

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm $^2$ for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.

Smart medical care is user-centric, medical information is the main line, and big data, Internet of Things, cloud computing, artificial intelligence, and other technologies are used to establish scientific and accurate information, as well as an efficient and reasonable medical service system. Smart medical plays an important role in alleviating doctor-patient conflicts caused by information asymmetry, regional health differences caused by irrational allocation of medical resources, and improving medical service levels. This article mainly introduces the remote care assistance system of emergency department based on smart medical and intends to provide some ideas and directions for the technical research of patients in emergency department receiving remote care. This paper proposes a research method for remote care assistance in emergency departments based on smart medical, including an overview of remote care based on smart medical, remote care sensor real-time monitoring algorithms based on smart medical, signal detection algorithms, and signal clustering algorithms for smart medical. Remote care in the emergency department assisted in research experiments. The experimental results show that 86.0% of patients like the remote care system based on smart medical studied in this paper.

Breast cancer is one of the leading causes of death in females and survival depends on early diagnosis and treatment. This paper applied machine learning techniques in prediction of breast cancer survival (dead or alive) using age, sex, length of stay, mode of diagnosis and location of cancer as predictors (independent variables). The data was obtained from the outpatient department of the University of Ilorin Teaching Hospital, Ilorin, Nigeria. The sample size of 300 consists of 175 females and 25 males who were admitted at the hospital and treated for breast cancer. The patients were later discharged or died. Adaptive boosting (AdaBoost) performed best out of the data mining models used in the classification in all the three cases where the target class is average over classes, alive or dead. The AdaBoost performed best with the classification accuracy and area under curve (AUC) of 98.3% and 99.9% respectively. Furthermore, a probe on the prediction by AdaBoost showed that the probability of dead due to breast cancer is 0.47, which the length of stay hugely contributed to the high probability, location of breast cancer and mode of diagnosis contributed minimally while age and sex contributed insignificantly. The high probability of breast cancer mortality predicted in this paper is a call for concern as early detection of breast cancer, routine breast examination and breast cancer awareness are crucial in increasing the probability of survival. The results can be used to design a decision support system that can increase the chances of breast cancer survival.

Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment.

At present, there is no method to predict or monitor patients with AMI, and there is no specific treatment method. In order to improve the analysis of clinical influencing factors of acute myocardial infarction, based on the machine learning algorithm, this paper uses the K-means algorithm to carry out multifactor analysis and constructs a hybrid model combined with the ART2 network. Moreover, this paper simulates and analyzes the model training process and builds a system structure model based on the KNN algorithm. After constructing the model system, this paper studies the clinical influencing factors of acute myocardial infarction and combines mathematical statistics and factor analysis to carry out statistical analysis of test results. The research results show that the system model constructed in this paper has a certain effect in the clinical analysis of acute myocardial infarction.

Out of all the changes to our daily life brought by the COVID-19 pandemic, one of the most significant ones has been the limited access to health services that we used to take for granted. Thus, in order to prevent temporary injuries from having lingering or permanent effects, the need for home rehabilitation device is urgent. For this reason, this paper proposes a cable-driven device for limb rehabilitation, CUBE2, with a novel end-effector (EE) design and autotuning capabilities to enable autonomous use. The proposed design is presented as an evolution of the previous CUBE design. In this paper, the proposed device is modelled and analyzed with finite element analysis. Then, a novel vision-based control strategy is described. Furthermore, a prototype has been manufactured and validated experimentally. Preliminary test to estimate home position repeatability has been carried out.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods. Ministry of Science and Technology, Taiwan

Dementia is a social problem in the aging society of advanced countries. Presently, 46.8 million people affected with dementia worldwide, and it may increase to 130 million by 2050. Alzheimer's disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase intensely due to the increasing number of patients due to the aging society. It isn't easy to cure AD, but early detection is crucial. This paper proposes a multi-class classification of AD, mild cognitive impairment (MCI), and normal control (NC) subjects using three dimensional-convolutional neural network with Support Vector Machine classifier. A cross-sectional study on structural MRI data of 465 subjects, including 132 AD patients, 181 MCI, and 152 NC, is performed in this paper. The highly complex and spatial atrophy patterns of the brain related to Alzheimer's Disease and MCI are extracted from structural MRI images using cascaded layers of the three dimensional convolutional neural network. The hectic process of segmentation and further extraction of handcrafted features is eliminated. The complete image is considered for the processing, thus incorporating every region of the brain for the classification. The features extracted using four cascaded layers of three dimensional-convolutional neural network are fed into the Support Vector Machine classifier. The proposed method achieved 97.77% accuracy which outperforms state of the art, and this algorithm is a promising indicator for the diagnosis of AD.

Purpose. To establish the correlation model between Traditional Chinese Medicine (TCM) constitution and physical examination indexes by backpropagation neural network (BPNN) technology. A new method for the identification of TCM constitution in clinics is proposed, which is trying to solve the problem like shortage of TCM doctor, complicated process, low efficiency, and unfavorable application in the current TCM constitution identification methods. Methods. The corresponding effective samples were formed by sorting out and classifying the original data which were collected from physical examination indexes and TCM constitution types of 950 physical examinees, who were examined at the affiliated hospital of Chengdu University of TCM. The BPNN algorithm was implemented using the C# programming language and Google’s AI library. Then, the training group and the test (validation) group of the effective samples were, respectively, input into the algorithm, to complete the construction and validation of the target model. Results. For all the correlation models built in this paper, the accuracy of the training group and the test group of entire physical examination indexes-constitutional-type network model, respectively, was 88% and 53%, and the error was 0.001. For the other network models, the accuracy of the learning group and the test group and error, respectively, was as follows: liver function (31%, 42%, and 11.7), renal function (41%, 38%, and 6.7), blood routine (56%, 42%, and 2.4), and urine routine (60%, 40%, and 2.6). Conclusions. The more the physical examination indexes are used in training, the more accurate the network model is established to predict TCM constitution. The sample data used in this paper showed that there was a relatively strong correlation between TCM constitution and physical examination indexes. Construction of the correlation model between physical examination indexes and TCM constitution is a kind of study for the integration of Chinese and Western medicine, which provides a new approach for the identification of TCM constitution, and it may be expected to avoid the existing problem of TCM constitution identification at present.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

Radiology experts often face difficulties in mammography mass lesion labeling, which may lead to conclusive yet unnecessary and expensive breast biopsies. This paper focuses on building an automated diagnosis tool that supports radiologists in identifying and classifying mammography mass lesions. The paper's main contribution is to design a hybrid model based on Pulse-Coupled Neural Networks (PCNN) and Deep Convolutional Neural Networks (CNN). Due to the need for large datasets to train and tune CNNs, which are not available for medical images, Transfer Learning (TL) was exploited in this research. TL can be an effective approach when working with small-sized datasets. The paper's implementation was tested on three public benchmark datasets: DDMS, INbreast, and BCDR datasets for training and testing and MIAS for testing only. The results indicated the enhancement that PCNN provides when combined with CNN compared to other methods for the same public datasets. The hybrid model achieved 98.72% accuracy for DDMS, 97.5% for INbreast, and 96.94% for BCDR. To avoid overfitting, the proposed hybrid model was tested on an unseen MIAS dataset, achieving 98.77% accuracy. Other evaluation metrics are reported in the results section.

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.

Background Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target for diseases like cancer. In recent years, the detection of base analogues in Oxford Nanopore Technologies (ONT) sequencing reads has become a promising new method to supersede existing single-molecule methods such as DNA fibre analysis: ONT sequencing yields long reads with high throughput, and sequenced molecules can be mapped to the genome using standard sequence alignment software. Results This paper introduces DNAscent v2, software that uses a residual neural network to achieve fast, accurate detection of the thymidine analogue BrdU with single-nucleotide resolution. DNAscent v2 also comes equipped with an autoencoder that interprets the pattern of BrdU incorporation on each ONT-sequenced molecule into replication fork direction to call the location of replication origins termination sites. DNAscent v2 surpasses previous versions of DNAscent in BrdU calling accuracy, origin calling accuracy, speed, and versatility across different experimental protocols. Unlike NanoMod, DNAscent v2 positively identifies BrdU without the need for sequencing unmodified DNA. Unlike RepNano, DNAscent v2 calls BrdU with single-nucleotide resolution and detects more origins than RepNano from the same sequencing data. DNAscent v2 is open-source and available at https://github.com/MBoemo/DNAscent. Conclusions This paper shows that DNAscent v2 is the new state-of-the-art in the high-throughput, single-molecule detection of replication fork dynamics. These improvements in DNAscent v2 mark an important step towards measuring DNA replication dynamics in large genomes with single-molecule resolution. Looking forward, the increase in accuracy in single-nucleotide resolution BrdU calls will also allow DNAscent v2 to branch out into other areas of genome stability research, particularly the detection of DNA repair.

Recent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

The incidence of glioma is increasing year by year, seriously endangering people’s health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the disease. Accurate segmentation of brain glioma has positive significance in medicine. However, due to the strong variability of the size, shape, and location of glioma and the large differences between different cases, the recognition and segmentation of glioma images are very difficult. Traditional methods are time-consuming, labor-intensive, and inefficient, and single-modal MRI images cannot provide comprehensive information about gliomas. Therefore, it is necessary to synthesize multimodal MRI images to identify and segment glioma MRI images. This work is based on multimodal MRI images and based on deep learning technology to achieve automatic and efficient segmentation of gliomas. The main tasks are as follows. A deep learning model based on dense blocks of holes, 3D U-Net, is proposed. It can automatically segment multimodal MRI glioma images. U-Net network is often used in image segmentation and has good performance. However, due to the strong specificity of glioma, the U-Net model cannot effectively obtain more details. Therefore, the 3D U-Net model proposed in this paper can integrate hollow convolution and densely connected blocks. In addition, this paper also combines classification loss and cross-entropy loss as the loss function of the network to improve the problem of category imbalance in glioma image segmentation tasks. The algorithm proposed in this paper has been used to perform a lot of experiments on the BraTS2018 dataset, and the results prove that this model has good segmentation performance.

This paper investigates cognitive computation of brain metabolism in maintenance hemodialysis patients with multimodal MRI therapy assessment. This paper constructs a cross-individual emotion recognition method using dynamic sample entropy pattern learning. The cross-individual emotion recognition was carried out on subjects using the EEG emotion dataset SEED. The experimental results show that the proposed dynamic sample entropy-based pattern learning has better performance in cross-individual emotion recognition and exhibits better generalization and generalization ability when compared with the results of existing related studies. The constructed cognitive computing method for cross-individual emotion state recognition achieves optimization and innovation of EEG emotion pattern recognition, which can effectively predict people’s mental emotion state from EEG signals. We also explore the value of diffusion-weighted magnetic resonance imaging and dynamic enhanced magnetic resonance imaging-based volumetric measurements in assessing the efficacy of neoadjuvant therapy in maintenance hemodialysis patients. We analyze and compare the results of different studies to find the best multimodal MRI to assess the efficacy of neoadjuvant therapy in maintenance hemodialysis patients. The use of ADC value growth rates to assess neoadjuvant efficacy provides the best diagnostic efficacy and allows the screening of patients who respond well to neoadjuvant therapy while avoiding the impact of two different b-value combinations commonly used to assess neoadjuvant efficacy.

This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

Phonocardiography is a dynamic non-invasive and relatively low-cost technique used to monitor the state of the mechanical activity of the heart. The recordings generated by such a technique is called phonocardiogram (PCG) signals. When shown visually, PCG signals can provide more insights of heart sounds for medical doctors. Thus, several approaches have been proposed to analyse these sounds through PCG recordings. However, due to the complexity and the high nonlinear nature of these recordings, a computer-assisted technique based on higher-order statistics HOS is shown to be, among these techniques, an important tool in PCG signal processing. The third-order spectra technique is one of these techniques; known as bispectrum, it can provide significant information to support physicians with an accurate and objective interpretation of heart condition. This technique is implemented and discussed in this paper. The implemented technique is used for the analysis of heart severity on nine different PCG recordings. These are normal, innocent murmur, coarctation of the aorta, ejection click, atrial gallop, opening snap, aortic stenosis, drum rumble, and aortic regurgitation. A unique bispectrum representation is generated for each type of heart sounds signal. Then, based on the bispectrum analysis, fifteen higher-order spectra HOS features such as the bispectral amplitude, the entropies, the moments, and the weighted center are extracted from each PCG record. The obtained HOS-features showed a well-correlated evolution with the increasing importance of heart severity leading therefore to a high potential in discriminating pathological PCG signals. One should know that, generally, classification of pathological PCG signals refers to the distinction between the presence of a pathology from its absence (binary response) while the discrimination considered in this paper provides an analogue response (value) which can vary from one pathology to another in an increasing or decreasing way.

Pneumothorax is potentially a life-threatening disease that requires urgent diagnosis and treatment. The chest X-ray is the diagnostic modality of choice when pneumothorax is suspected. The computer-aided diagnosis of pneumothorax has received a dramatic boost in the last few years due to deep learning advances and the first public pneumothorax diagnosis competition with 15257 chest X-rays manually annotated by a team of 19 radiologists. This paper describes one of the top frameworks that participated in the competition. The framework investigates the benefits of combining the Unet convolutional neural network with various backbones, namely ResNet34, SE-ResNext50, SE-ResNext101, and DenseNet121. The paper presents a step-by-step instruction for the framework application, including data augmentation, and different pre- and post-processing steps. The performance of the framework was of 0.8574 measured in terms of the Dice coefficient. The second contribution of the paper is the comparison of the deep learning framework against three experienced radiologists on the pneumothorax detection and segmentation on challenging X-rays. We also evaluated how diagnostic confidence of radiologists affects the accuracy of the diagnosis and observed that the deep learning framework and radiologists find the same X-rays to be easy/difficult to analyze (p-value

It is necessary to solve the problem of food corruption and oxidation to improve food quality. Peptides are a good candidate to solve the above problems. In this paper, a machine learning method was used to construct an antioxidant peptide classification model based on the pseudo-amino acid composition and motifs of peptides as input features. The AUC of PseAAC-dipeptide-motif hybrid model is 0.939 and the average precision score is 0.947, which is the best among all models in this paper. Besides, the classification threshold has been increased to make the model precision above 0.95. Then, the model was used as predictor to discover potential antioxidant peptides from a random peptide dataset. According to the predicted results, 5 potential antioxidant peptides (PSGK, LKPQ, GRP, QCQ, QGM) were synthesized to determine their DPPH radical-scavenging activity and the total antioxidant capacity (T-AOC). The experimental results show that QCQ has strong antioxidant properties, its T-AOC value is 9.59U/mg prot, and the DPPH scavenging activity is 95.52% at concentration of 125 μg/mL. Meantime, the predictor can be used to mine multifunctional peptides with antioxidant function. In general, the predictor is an effective tool for discovering peptides with antioxidant function.

Objective: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results are obtained for parasite detection in thick blood smears for a smartphone application using deep learning methods. Significance: Automated parasite detection running on smartphones is a promising alternative to manual parasite counting for malaria diagnosis, especially in areas lacking experienced parasitologists.

Hierarchical actuators are comprised of multiple individual actuator elements arranged into a system, resulting in improved and expanded performance. Natural muscle tissue is a complex and multi-level example of hierarchical actuation, with its hierarchy spanning from the micrometer to the centimeter scale. In addition to a hierarchical configuration, muscle tissue exists in varying geometric arrangements. Pennate muscle tissue, denoted by its characteristic fibers extending obliquely away from the muscle tissue line of action, leverages geometric complexity to transform the relationship between fiber inputs and muscle tissue outputs. In this paper, a bioinspired hierarchical pennate actuator is detailed. This work expands on previous pennate actuator studies by deriving constitutive force, contraction, and stiffness models for a general pennate actuator, where the constituent fibers can be constructed from any linear actuator. These models are experimentally validated by studying a pennate actuator with McKibben artificial muscles constituting the actuator fibers. McKibben artificial muscles are used because they have a high force-to-weight ratio and are inexpensive to construct, making them an attractive candidate for hierarchical actuators and mobile robotics. Using the derived constitutive models, general pennate actuator performance is better understood by analyzing the transmission ratio, blocked force, and free contraction. Loaded contractions and stiffness during isotonic and isobaric contractions are also explored. The results allow for informed design decisions and an understanding of the associated tradeoffs when recreating the remarkable properties of pennate musculature. Future work will leverage the results of this paper to create an adaptive pennate actuator that is capable of changing configuration in response to force, contraction and stiffness demands.

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient’s individualized risk assessment as screening tool for use in clinical practice. ispartof: Frontiers In Bioengineering And Biotechnology vol:8 ispartof: location:Switzerland status: Published online

During the first months of life, babies can be affected by congenital nystagmus, an ocular-motor disease making visual acuity decrease. Electrooculography (EOG) and Infrared-oculography are utilized in order to perform eye-tracking of patients, giving the possibility to extract from the signals several useful features. In the past years, different algorithms were used to perform the detection of events on these features and many researchers studied the relationships between the features and physiological values such as visual acuity and variability of eye-positioning. In this paper, machine learning techniques were used to predict visual acuity and the variability of eye positioning using features extracted from EOG. The EOG of 20 patients was acquired, signals underwent a pre-processing, and some parameters were extracted through a custom-made software. Frequency, amplitude, intensity, nystagmus foveation periods and both amplitude and frequency of baseline oscillation were the features used as input for the algorithms. Knime analytics platform was employed to perform a predictive analysis using Random Forests, Logistic Regression Tree, Gradient boosted tree, K nearest neighbour, Multilayer Perceptron and Support Vector Machine. Finally, some evaluation metrics were computed employing a leave one out cross validation. Considering the coefficient of determination, visual acuity achieved values between 0.67 and 0.85 while variability of eye positioning ranged from 0.62 to 0.79. These results were compared with past analysis with the exact same aims and dataset, obtaining a greater value as regards the variability of eye positioning and comparable results exploiting all the features related to nystagmus as regards the visual acuity. This paper showed the feasibility of a regression analysis performed through machine learning algorithms in detecting relationships among variables related to congenital nystagmus. © 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%., This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.

Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.