Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

This study evaluates the effectiveness of various non-echocardiographic medical biomarkers, integrated with machine learning (ML) models, for classification of different types of Heart Failure (HF), specifically heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). This differentiation is critical due to the distinct pathophysiology and management strategies required for these heart failure subtypes. A retrospective clinical dataset was collected from three medical centers in Serbia, comprising anonymized records of 481 heart failure patients ($221 \text{HFrEF}, 260 \text{HFpEF}$). The dataset included three primary types of variables: health records, laboratory test results, and electrocardiogram (ECG) data. Data preprocessing involved standardization of laboratory values and Multivariate Imputation by Chained Equations (MICE) to address missing parameters. Five different machine learning algorithms were employed, with an$80 / 20$train-test split and exhaustive Grid Search for hyperparameter optimization: Decision Tree, Random Forest, eXtreme Gradient Boosting Tree (XGBoost), Multilayer Perceptron (MLP), and Gradient Boosting Tree. Model performance was assessed using accuracy, precision, recall, and$\mathbf{F 1}$-score. The results indicate that ensemble methods, specifically Gradient Boosting Tree and Random Forest, consistently achieved superior performance across various data subsets. Notably, the combination of Health Records and ECG data yielded the highest predictive performance, with the Gradient Boosting Tree model achieving an F1-score of 0.7736 and a recall of 0.8039. Conversely, models trained predominantly on, or solely with, laboratory testing features consistently exhibited lower performance, suggesting that a broad inclusion of these biomarkers may introduce noise or redundancy for this specific classification task. These findings demonstrate that non-echocardiographic biomarkers can be effectively leveraged by machine learning models to differentiate between HFrEF and HFpEF, offering a promising diagnostic tool.

Heart failure (HF) is one of the most common medical conditions around the world in the modern age. Patients with HF struggle with reduced quality of life and have a greater risk of death. For these reasons it is important for HF to be diagnosed early before it starts to majorly impact patients and cause major concern for death. Left ventricular ejection fraction (LVEF) is one of the most important features which can be used to further classify HF patients into patients with reduced ejection fraction (HFrEF), mid-range ejection fraction (HFmrEF) and preserved ejection fraction (HFpEF). In this paper we describe a machine learning based pipeline for multistage classification of patients according to presence of HF and degree of reduced ejection fraction. Two separate pipelines are created in the scope of our research. Both pipelines classify patients based on whether HF is present or not. The first pipeline classifies HF patients into 3 classes based on their ejection fraction into reduced, mid-range and preserved classes. The second pipeline conducts classification of patients into LVEF$<50 {\%}$and LVEF$>50 {\%}$based on available features, followed by a second classification of HFrEF and HFpEF classes. HF classification model achieved 97% accuracy and$\mathbf{9 8 \%} \mathbf{F 1}$score for the confirmed HF class. The 3-class classification model achieved an overall prediction accuracy of 82% with F1 scores of 90%, 51% and 86% for HFpEF, HFmrEF and HFrEF respectively. The final two stage classification model achieved an overall accuracy of 96% and 87% for LVEF$>50$% versus LVEF$<50$% and mid-range versus reduced ejection fraction respectively. In the case of the twostage classification model F1 scores of$\mathbf{9 2 \%, 9 5 \%}$and 97% were achieved for HFpEF, HFmrEF and HFrEF respectively.

The evolution of information and communication technologies has affected all fields of science, including health sciences. However, the rate of technological innovation adoption by the healthcare sector has been historically slow, compared to other industrial sectors. Innovation in computer modeling and simulation approaches has changed the landscape in biomedical applications and biomedicine, paving the way for their potential contribution in reducing, refining, and partially replacing animal and human clinical trials. In Silico Clinical Trials (ISCT) allow the development of virtual populations used in the safety and efficacy testing of new drugs and medical devices. This White Paper presents the current framework for ISCT, the role of in silico medicine research communities, the different perspectives (research, scientific, clinical, regulatory, standardization, data quality, legal and ethical), the barriers, challenges, and opportunities for ISCT adoption. In addition, an overview of successful ISCT projects, market-available platforms, and FDA- approved paradigms, along with their vision, mission and outcomes are presented.

Atherosclerotic disease on peripheral arteries, commonly known as peripheral artery disease represents accumulation of cholesterol in peripheral arteries. It causes the formation of different types of depositions called plaques which cause thickening of the arterial walls resulting in reduced blood flow to lower extremities, in case of femoral arteries, upper extremities, in case of brachial artery or brain in case of carotid artery. Current research shows that approximately 8.5 million Americans, aged over 40 years, are affected by peripheral artery disease while one fourth of them falls into a severe category. For these reasons, an early detection of the disease is important. In order to detect the disease, arteries need to be imaged properly. In the recent years, an increase of optical coherence imaging has occurred due to the ease of use and the ability to detect tissue morphology which is extremely important for the detection of the disease. From the tissue morphology, it is of most importance to observe the tissue between lumen and intimal layer of the artery, since the majority of the plaques form in that region, but it is also important to observe the region between intima and adventitia (the outer wall of the artery) since the plaque can penetrate the intimal layer as well. In this paper, a deterministic approach for the detection of the adventitia is described based on the previously detected intimal layer of the artery. The proposed method is evaluated on optical coherence tomography images from 8 specimens of porcine femoral arteries. The results show that the detection of adventitia is possible on this image modality with Dice coefficient 0.9805 and Hausdorff distance 0.1 mm.

Electrospinning is a simple and cost-effective method for producing smooth and thin nanofibers. The relationship between experimental parameters and the properties of the obtained fibers is important to investigate due to their potential applications. By varying the parameter values, the desired fiber diameter and morphology can be achieved. In this study, the effect of process parameters, applied voltage and flow rate, on the morphology and diameter of fibers obtained through the electrospinning of a PCL and PEG polymer solution was examined. The results show that these parameters affect fiber diameter, with an increase in voltage generally leading to a decrease in diameter, while an increase in flow rate generally results in thicker fibers.

Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Traditionally image segmentation was used on 2D imaging data, but due to the increased usage of 3D volumetric data there is a need for 3D segmentation techniques that could utilize spatial information contained in these volumes. One of the fields where there is a great amount of 3D data is microscopy. This paper introduces convolutional neural network based on 3D U-net architecture for segmentation of confocal microscopy images of cells in an in vitro sprouting angiogenesis model. Developed model contains 4 layers where each encoder block contains two 3D convolutional layers, Batch Normalization, ReLU activation function and 3D max pooling layer, while each decoder block contains upconvolution, skip connections and two 3D convolutional layers. Preprocessing of this data resulted in the volumes of shape 256 × 256 × 256 voxels which were used for training of the developed model. The model achieves great segmentation results as showed by Jaccard index value of 94.52% and Dice coefficient value of 99.31% compared to the preprocessed dataset. Even when segmentation results are compared to the original dataset, model still achieves respectable results of 84.22% Jaccard index and 88.18% Dice coefficient. This introduction of automatic 3D image segmentation could greatly reduce the time required for data preparation, while achieving high degree of segmentation accuracy.

Despite advances in heart research, knowledge about the cardiac conduction system and sinoatrial node (SAN) remains limited, with no effective treatments for SAN dysfunction currently available. Artificial pacemakers and defibrillators are commonly used but can lead to complications, emphasizing the need for better strategies and therapies. Human induced pluripotent stem cells (hiPSCs) offer a promising source for generating SAN-like cells, which can help in studying and treating genetic arrhythmias and modeling cardiac diseases. Publicly available transcriptomic datasets were sourced from the Gene Expression Omnibus (GEO) repository using specific keywords based criteria. Differential gene expression analysis was conducted using the$\mathbf{R}$software environment with the DESeq2 package, involving pre-filtering and normalization of raw counts. Our bioinformatic strategy facilitates disease modeling and drug discovery by analyzing expression profiles. Principal component analysis revealed distinct cell populations, suggesting different responses to drugs, while Gene Ontology enrichment highlighted affected biological pathways for targeted therapy. This method, applicable to various transcriptomic databases, aids in identifying potential therapies.

In this paper, a hybrid numerical model is used to simulate the growth of cancer cells in-vitro. This model combines the agent-based model (ABM) that is used to simulate the progression of individual cancer cells and the lattice Boltzmann method (LBM) that is used to simulate the change of distribution of nutrients within the system, that is used by these cells within the observed domain. Results of a numerical simulation, that include the change of number of viable, necrotic and apoptotic cells over time are presented. The parameters of the numerical model can be estimated using experimental data and this can provide additional quantitative information about the behavior of cancer cells and about the influence of diverse drug treatments considered experimentally on these parameters. Numerical simulations like the one presented in this paper can be very useful in planning future experiments as they can provide means for a more in-depth analysis of a variety of phenomena occurring during the cell life-cycle.

The action potential is a pivotal electrical phenomenon that governs the contraction of cardiac muscle cells, known as cardiomyocytes. This rapid shift in membrane potential is orchestrated by the precise movement of ions across the cell membrane through specific ion channels. In the heart, action potentials are indispensable for initiating and coordinating the contractions of cardiac chambers, ensuring effective blood circulation throughout the body. The left ventricle, responsible for propelling oxygenated blood into the aorta and systemic circulation, is particularly critical. Deviations in the left ventricular action potential can result in severe cardiac conditions, including arrhythmias, heart failure, and sudden cardiac death. Consequently, an accurate understanding and modeling of the left ventricular action potential are essential for elucidating the mechanisms underlying these diseases and developing effective treatments. This paper provides a comprehensive overview of methodologies used to simulate the action potential within the left ventricle. It examines the historical evolution of electrophysiological models, foundational theories of cardiac action potentials, and their specific application to the left ventricle. The review encompasses the clinical relevance of these models and an analysis of simulation outcomes. This paper also explores the application of machine learning (ML) to enhance the accuracy and efficiency of action potential calculations within the left ventricle, highlighting various ML techniques and their potential impact on cardiology.

The use of image segmentation is widespread in many different fields of research including medicine, biology, computer vision and others. In most cases, image segmentation is done through the use of supervised learning approaches, which utilize ground truth segmentation masks. However, in biomedical research, segmentation masks are often not available, which leads to the use of unsupervised segmentation approaches. In these situations, filtering paired with region expansion and reduction and edge detection techniques are used, which in some cases require time-consuming manual parameter tuning, in order to achieve satisfactory results. This paper implements fully unsupervised image segmentation based on a deep learning convolutional neural network to segment cell geometries from confocal microscopy images of an in vitro model of angiogenesis. The proposed network architecture is a W-Net which contains a single U-Encoder and U-Decoder, both of which contain 3 convolution blocks and 3 deconvolution blocks, with 2 convolution layers and one max-pooling or up-convolution layer respectively. The results were evaluated using available ground truth images and achieved a pixel wise classification accuracy score of 98.73% and 73.71% intersection over union. The achieved high degree of accuracy shows great promise in cell segmentation without a need for ground truth masks over time and increases the accuracy of calculations of exerted forces.

Colorectal cancer is a leading cause of cancerrelated deaths, and understanding its molecular mechanisms is key to improving treatments. This study applies machine learning algorithms to analyze the expression of genes related to redox balance, apoptosis, and cell migration in HCT-116 colorectal cancer cells treated with chemotherapeutics (5fluorouracil, oxaliplatin, irinotecan, and leucovorin). The analyzed genes include those for redox homeostasis (GPX1, GPX2, GPX3, GPX4, TXNRD1, GSTP1, NFE2L2, NFKB1, HIF1A), apoptosis (CASP3, CASP8, CASP9, FAS, BCL-2, BAX), and genes coding cytoskeleton proteins (CDH1, CTNNB1, CDH2). Machine learning models, such as decision trees and random forests were used to analyze gene expression changes based on qPCR data. Notably, GPX4 and BAX were linked to chemoresistance, while CDH1 upregulation suggested an effect on cancer cell migration. This study demonstrates the potential of machine learning in identifying gene expression patterns and aiding in the development of personalized CRC treatments.

This study showcases the synthesis of silver nanoparticles (AgNPs) by the utilization of Borovnica extract (Vaccinium myrtillus) and Bosiljak extract (Ocimum basilicum) in a simple and non-toxic one-step procedure. The method is described as eco-friendly, uncomplicated, and economical, utilizing a water-based plant extract that acts as both a reducing and stabilizing agent for AgNPs. The VM-AgNPs and OB-AgNPs were characterized by UV-Vis spectroscopy, field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM). UV-Vis spectroscopy detected surface plasmon resonance (SPR) at a wavelength of 305 nm and 344 nm in the VM-AgNPs and OB-AgNPs solutions, respectively. The investigation of VM-AgNPs and OB-AgNPs using FESEM revealed the presence of oval-shaped NPs with average sizes of 52 nm and 55 nm, respectively. While TEM analysis showed similar observation with average size of 80 nm and 35 nm, respectively. Both VM-AgNPs and OB-AgNPs showed cytotoxic properties against healthy lung pleura fibroblasts (MRC-5), resulting in a reduction in cell viability that was dependent on the dosage. The inhibitory concentration (IC50) in VM-AgNPs and OB-AgNPs treated group was calculated to be 25.89 ± 1.41 μg/ml and 8.41 ± 1.00 μg/ml, respectively. Moreover, the study investigated the involvement of reactive oxygen species (ROS) in the toxicity to cells. It was noted that both NPs induce oxidative stress over time in MRC-5 cell lines in a dose-depended manner.

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management.

Recent advances in regenerative medicine provide encouraging strategies to produce artificial skin substitutes. Gelatin scaffolds are successfully used as wound dressing materials due to their superior properties, such as biocompatibility and the ability to mimic the extracellular matrix of the surrounding environment. In this study, five gelatin combination solutions were prepared and successfully electrospun using an electrospinning technique. After careful screening, the optimal concentration of the most promising combination was selected for further investigation. The obtained scaffolds were crosslinked with$\mathbf{2 5 \%}$glutaraldehyde vapor and characterized by Scanning Electron Microscopy (SEM). The incorporation of antibiotic agents such as ciprofloxacin hydrochloride and gentamicin sulfate into gelatin membranes improved the already existing antibacterial properties of antibiotic-free gelatin scaffolds against bacteria. Also, the outcomes of the in vivo model study revealed that skin regeneration was significantly accelerated with gelatin/ciprofloxacin scaffold treatment. Finally, the combination of gelatin's extracellular matrix and antibacterial agents in the scaffold suggests its potential for effective wound healing treatments, emphasizing the importance of gelatin scaffolds in tissue engineering.

Falls among older adults, especially those with complex comorbidities like cognitive impairment, present a considerable challenge in rehabilitation settings. Conventional treatment approaches often fall short in addressing these multifaceted needs, underscoring the demand for comprehensive, individualized rehabilitation solutions. Augmented Reality (AR) technologies are emerging as promising tools to meet these challenges, offering tailored interventions that respond to each patient's unique requirements. AR-based systems facilitate immersive and interactive rehabilitation, enabling patients to engage in activities that support both physical and cognitive health. Additionally, AR allows healthcare providers to offer real-time, remote support, expanding access to care for underserved populations. This paper introduces the Virtual Coach Platform, an AR-based rehabilitation system designed to address both the physical and cognitive needs of patients. The platform comprises an AR headset, which projects a 3D virtual coach, and an Android-based software application that integrates physical exercises, cognitive games, and exergames. By examining the challenges and advantages of this system, we highlight how the Virtual Coach Platform can foster greater patient adherence and engagement.

Medical imaging plays an important role in medicine today, assisting in illness diagnosis and therapy. For limited medical image datasets, training from scratch is not an option, hence transfer learning emerges as a solution, with ImageNet weights being utilized as initial weights, followed by fine-tuning. This paper takes a different approach by introducing transfer learning approach with pretrained architecture DenseNet121 with CheXNeXt weights. Collected dataset consisted of 227269 X-ray images from public databases and 684 chest X-ray images from a retrospective study conducted in the University Clinical Center of Kragujevac and includes information on atelectasis, cardiomegaly, parenchymal consolidation, edema, effusion, emphysema, fibrosis, hiatus hernia, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, viral pneumonia in the form of Covid-19, tuberculosis as well as tumors in the form of mass and nodules. The results show that the model is able to distinguish between the healthy and diseased lungs with average AUC of 0.91 (the lowest AUC of 0.8 for emphysema and the highest AUC for of 0.99 for pneumonia and 0.98 for COVID-19). Although the results seem promising, additional fine tuning may be necessary to improve other metrics. Future research will focus on this aspect, as well as on creating a glass box system for classification.

Huxley's muscle model, originally devised for modeling non-uniform contractions, possesses a noteworthy drawback rooted in its substantial computational demands, particularly evident in the context of multi-scale finite element simulations. In order to address this limitation, we have created surrogate models of Huxley's muscle model. These surrogate models emulate the behavior of the original model while reducing the computational demands in terms of execution time. In this paper, we present the construction of surrogate models using physics-informed neural networks. Besides the precision of neural network predictions, it is also important for the neural network to have a small number of weights in order to be computationally efficient. To optimize the size of the neural network along with the precision of its predictions, we performed Bayesian Optimization. Our physics-informed neural network predicts the probabilities of cross-bridge formation, based on which, force and stiffness can be calculated and used during finite element analysis. In our work, we also present the procedure to integrate a physics-informed neural network into the finite element analysis framework at the micro-level of multi-scale simulation.

Vehicle comfort in oscillatory conditions is a multifaceted phenomenon affected by various factors including road conditions, driving speed, and driving mode, among others. Vibrations experienced during driving, irrespective of their intensity or waveform, significantly impact driving comfort. The Seat-to-Head Frequency Response Function (STHT) represents a complex connection between head movements and vibrations transmitted through the seat and headrest interface. In this study, an artificial neural network model was created to replicate the STHT function based on experimental data collected from twenty healthy male participants.

The main aim of this study was to evaluate the impact of simulated aortic stenosis on velocity and shear stress distribution within the patient-specific heart model by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific model of heart, including surrounding arterial and vein structures, was reconstructed based on Computed Tomography (CT) scan images in order to obtain the 3D FE mesh. Computational Fluid Dynamics (CFD) analysis was performed, with applied equivalent material characteristics and boundary conditions. Using one patient-specific heart model with clinically confirmed hypertrophic cardiomyopathy, three different cases were simulated: (i) without aortic stenosis, (ii) with 30% of aortic stenosis (mild aortic stenosis), and (iii) with 70% of aortic stenosis (severe aortic stenosis). The initial results of the study (velocity and shear stress distribution) were quantified concerning anatomical patient's structures and simulating different degrees of aortic stenosis to analyse the blood flow patterns, as well as the correlation between shear stress and aortic and left ventricular remodelling. It was found that gradient of shear stress distribution increases with stenosis degree, especially in the ascending aorta which can lead to different aortopathies and endothelial diseases. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the heart and aortic stenosis that is needed to achieve improvements in surgical repair techniques and presurgical planning.

Localization of lumbar discs in magnetic resonance imaging (MRI) is a challenging task, due to a vast range of shape, size, number, and appearance of discs and vertebrae. Based on a review of the cutting-edge methods, the majority of applied techniques are either semi-automatic, extremely sensitive to change in parameters, or involve further modification of the results. All of the above represents a motivation for implementing deep learning-based approaches for automatic segmentation and classification of disc herniation in MR images. This paper proposes a complete automated process based on deep learning to diagnose disc herniation. The methodology includes several steps starting from segmentation of region of interest (ROI), in this case disc area, bounding box cropping and enhancement of ROI, after which the image is classified based on convolutional neural network (CNN) into adequate classes (healthy, bulge, central, right or left herniation for axial view and healthy, L4/L5, L5/S1 level of herniation in sagittal view). The results show high accuracy of segmentation for both axial view (dice = 0.961, IOU = 0.925) and sagittal view (dice = 0.897, IOU = 0.813) images. After cropping and enhancing the region of interest, accuracy of classification was 0.87 for axial view images and 0.91 for sagittal view images. Comparison with the literature shows that proposed methodology outperforms state-of-the-art results when it comes to multiclassification problems. A fully automated decision support system for disc hernia diagnosis can assist in generating diagnostic findings in a timely manner, while human mistakes caused by cognitive overload and procedure-related errors can be reduced.

This paper presents a short review of the piezoelectric materials in energy harvesting. Energy harvesting principle, as the method for obtaining energy from environment has been described. Materials and material combinations for creating an energy harvesting composites are discussed, such as ceramic- and polymer-based composites and their mechanical properties. The list of the mostly used piezoelectric materials is presented and elaborated. Possible applications of the energy harvesting materials are discussed, including nanogenerators, biosensors and biomedical applications.

Since the novel SARS-CoV-2 virus appeared, interest in developing epidemiological mechanisms that would help in prevention of its spread has increased. Epidemiological models are the most important mechanisms for examining the spread of the virus. For that purpose, we propose deep learning approach, LSTM neural network model. LSTM is a special kind of neural network structure capable of learning long-term dependencies in sequence prediction problems. The model was fed with official statistical data available online for Belgium in the period of March 15th, 2020 to March 15th, 2021. Results show that LSTM is capable of predicting in long-term manner with the low values of RMSE and MAE. Higher values of RMSE and MAE are observed in the infected cases (RMSE was 397.23 and MAE was 315.35) which is expected due to thousands of infected people per day in Belgium. In future studies, we will include more phenomena, especially medical intervention and asymptomatic infection, in order to better describe the COVID-19 spread and development.

It is important to consider the blood flow pattern when planning vascular interventions of atherosclerotic plaques. Large scale computer modeling can be very helpful in this case. The software presented in this paper numerically models blood flow through patient-specific blood vessels. Lattice Boltzmann method was used to simulate blood flow. The principles of GPU (Graphics Processing Unit) programming are applied during implementation and the developed software was parallelized using the CUDA (Compute Unified Device Architecture) and optimized to run on a multi-GPU cluster using the MPI approach. Using the multi-GPU infrastructure, numerical simulations can utilize larger amount of memory resources for the computation, making the level of reality of the models an order of magnitude higher. Execution of the presented software enables fast and reliable case-studies and parametric analyses useful for medical decision-making. The presented software can give medical professionals fast quantitative information about fluid flow in diseased arteries and assist them in selecting the most appropriate treatment.

The brain controls the heart through the sympathetic and parasympathetic branches of the autonomic nervous system. It consists of multisynaptic pathways from myocardial cells back to peripheral ganglionic neurons and further to central preganglionic and premotor neurons. Still, there are no reliable cardiovascular markers of the sympathetic tone and of the sympathetic-parasympathetic balance. It is necessary to understand the interaction between the brain and the heart in order to make early detection and treatment of pathological changes in the brain-heart interaction. In this study we present a detailed electro-chemo-mechanical model of heart and torso, so as to simulate the three principal modes of actions of drugs for cardiomyopathy: (i) modulating calcium transients, (ii) changing kinetics of contractile proteins, (iii) changing the macroscopic structure or its boundary conditions. Heart model geometry included seven different regions. Monodomain model of modified FitzHugh-Nagumo model of the cardiac cell was used. Six electrodes were positioned on the chest to model the precordial leads and the results were compared with real clinical measurements. Inverse ECG method was used to optimize potential on the heart. A whole heart was embedded in the electrical activity throughout the torso environment, with spontaneous initiation of activation in the sinoatrial node, incorporating a specialized conduction system with heterogeneous action potential morphologies throughout the heart. We included body surface potential maps in a healthy subject during progression of ventricular activation in nine sequences. The electrical model was coupled with a mechanical model with orthotropic material properties obtained from the experiments of Holzapfel. In future research we will be more focused on in silico clinical trials with the aim to compare some clinical pathology findings on the body surface with standard 12 ECG electrode measurements.

The world stent market has an estimated value of €6.4 billion, of which 37% is generated in the US and 10% in the EU. Coronary stents are now the most commonly implanted medical devices, with more than 1 million implanted annually. Coronary stents are currently the most widely used for treating symptomatic coronary disease. In this study, the traditional approach for today's clinical trials with only 10% success rate was described. Within the EU funded project InSilc (www.insilc.eu) was developed the innovative platform for designing, developing and assessing coronary stents. It consists of separate modules and some of them can be used as a standalone tool. Description of each module was given. Cost-effectiveness analysis described the calculation method of the prices of each module as well as platform as a whole, per one stent simulation. The average cost per patient for the execution of a real clinical trial was calculated. The calculated price for in silico trials is below breakeven point in comparison to real clinical trial.

Emitted fraction (EF) is one of the critical quality attributes of dry powders for inhalation (DPIs). Traditionally, different in vitro methods have been used for the assessment of DPIs EF. However, the evolution in computer-based (in silico) methods led to the development of special fields, such as Computational Fluid Dynamics (CFD) coupled with fluid-particle dynamics models e.g., Discrete Phase Model (DPM) as a useful alternative for the assessment of DPIs aerodynamic performance. The aim of this study was to design a CFD-DPM model for the prediction of model DPIs EF, and assess the prediction power of this method by comparing the in silico prediction results with in vitro determined EF values, obtained by three different methods. The EFs of the solid lipid microparticles (SLM) DPIs were determined in vitro by Twin stage impinger, Next generation impactor and Fast Screening Impactor. CFD-DPM model was successfully designed, and then the simulation results indicated the percentage of particles that remained in the inhaler. Based on those data, DPI EFs were calculated to be in the range of 83 % and 92%, indicating that CFD-DPM simulations were able to catch the differences between five SLM DPI formulations. In addition, CFD-DPM predicted the regional particle deposition in the inhaler, which cannot be precisely determined based on in vitro experiments. CFD-DPM predicted EF values were generally comparable to the EF values obtained by three in vitro methods, although some differences were observed between in vitro and in silico values. Therefore, it can be concluded that although additional improvements of CFD-DPM designed model are still necessary in order to be able to precisely describe aerodynamic performance of SLM DPIs, CFD-DPM modeling can be considered as a very useful tool in DPIs development.

The paper describes experiences from building and cloudification of the in-silico research platform SilicoFCM, an innovative in-silico clinical trials' solution for the design and functional optimization of whole heart performance and monitoring effectiveness of pharmacological treatment, with the aim to reduce the animal studies and the human clinical trials. The primary aim of cloudification was to prove portability, improve scalability and reduce long-term infrastructure costs. The most computationally expensive part of the platform, the scientific workflow manager, was successfully ported to Amazon Web Services. We benchmarked the performance on three distinct research workflows, each of them having different resource requirements and execution time. The first benchmark was pure performance of running workflow sequentially. The aim of the second test was to stress-test the underlying infrastructure by submitting multiple workflows simultaneously. The benchmark results are promising, painting the infrastructure launching overhead almost negligible in this kind of heavy computational use-case.

Heart failure (HF) is a condition that affects mostly older populations. It can be treated with different medications, and one of them is Entresto. This is a medication which is consisting of two drugs, sacubitril (SAC) and valsartan (VAL). Here, in this study, are performed molecular docking simulations in order to examine the inhibitory potency of SAC and VAL towards neprilysin (NEP) and angiotensin II receptor (AT2), respectively. The achieved thermodynamic parameters shows that SAC and VAL can bind to targeted protein, and inhibit NEP and AT2. The best binding sites are determined. Also, the amino acids responsible for binding are identified.

Most cardiac diseases and disorders occur in the left ventricle. Numerical methods can give an insight into the mechanical response of the left ventricle under different conditions, before the execution of clinical trials and experiments. Before we use the finite element method to analyze the behavior of the left ventricle, a geometrical model has to be generated. In our work, we generated a left ventricle model from echocardiographic data. We manually extracted contours of the inner and outer surface of the left ventricle and applied our algorithm to generate the 3D model. This semi-automatic model generation enables the usage of patient-specific geometries for finite element analysis of the left ventricle.

Preclinical experimentation demands for highly reliable and physiologically-relevant systems capable of recapitulating the complex human physiology. Further technological advances are in great need for improving our understanding about critical biological processes involved in tissue development or cancer progression, and for the discovery and screening of novel pharmacological drugs. Traditional in vitro models, albeit widely employed, fail to reproduce the complexity of the native scenario. Similarly, in vivo animal models poorly mimic the human condition and they are ethically questionable. During the last two decades, a new paradigm in preclinical modelling has emerged aiming to solve the limitations of the previous methods. The combination of advanced tissue engineering, cell biology and nanotechnology, has resulted in the development of cutting-edge microfluidics-based models with an unprecedented ability to recreate the native habitat of cells within a microengineered chip. Among the diverse variety of micro- and bio- fabrication techniques, UV-photolithography and soft lithography are considered the gold-standard methods for the fabrication of microfluidic chips to their simplicity, versatility, and rapid prototyping. In this paper, we describe a protocol for the fabrication of a microfluidic chip by UV-photolithography and replica molding, and an example of its use in cell migration assays.

The heart is a complex organ which produces mechanical force needed for the blood flow. Electrical signals are transformed into active stresses which contract the heart muscle and pump the blood out from the left ventricle. Therefore, comprehensive numerical procedure has to be established in order to simulate this process and to investigate the effects of different drugs on heart behavior. We here present application of the finite element (FE) computational model for simulation of heart beat cycle of the parametric left ventricle model. We are using Hunter excitation model for active, and direct experimental constitute relations for passive mechanical stresses. Additionally, computational model includes hysteretic and compressible behavior according to the experimental investigations. Applicability of our computational model is demonstrated using parametric left ventricle model which includes inlet mitral and outlet aortic valve cross-sections. With using different boundary conditions and prescribed values, this model has potential to mimic the effects of different drugs on heart beat cycle.

Assessment of the spinal disorders is a notoriously difficult problem, even in controlled environments where the patients are instructed to stand upright. The method presented here considers the analysis of the mathematical curvature of the scaled and interpolated spinal line, in both the sagittal and frontal planes. Although the number of assumptions for spine normality is kept to a (reasonable) minimum, we demonstrate good detection of sharp or otherwise unnatural local bending in adolescent spinal alignments.

The paper describes mathematical model and numerical simulation of Mason-Weaver equation using finite difference method, FDM, for simulation of sedimentation process. Different FDM schemes have been developed and tested for several different initial conditions. Possible issues with numerical convergence and conservation of concentration are explained. Performed analysis can be important for any numerical simulation that captures sedimentation process. The results of this research can be further used in modelling epithelial cell behavior and lung-on-a-chip systems.

Femoro-popliteal “by-pass” is indicated in the advanced stage of peripheral arterial occlusive disease. Indications for surgical treatment are set on the basis of the clinical picture, “ankle-brachial index” and angiographic findings. By the method of finite element analysis, three-dimensional models can be made on the basis of scanning angiography, on which we can measure different physical quantities and calculate the value of the “ankle-brachial index”. The aim is to show the hemodynamics of arteries by the method of finite element analysis (FEA) based on preoperative and postoperative scan angiography as well as physical quantities that can be measured in this way. In this review, the hemodynamics of femoro-popliteal “by-pass” on the preoperative and postoperative model are presented. The models obtained by FEA show: pressure, shear stress, velocities, and streamlines. Pressure, “ankle-brachial index”, compared with the values measured on the patient, with FEA results preoperatively and postoperatively. Postoperatively, higher values of pressure and “ankle-brachial index” were measured on the patient and on the models. The values shown in the models are significantly correlated with the values measured on the patient. Shear stress and velocity values are significantly reduced on postoperative models. The streamlines show a dominant anterior tibial artery. The values of physical quantities measured on the patient and on the models obtained by the FEA method correlate to a significant extent.

Calculation of turbulent fluid flow in this paper is performed using two-equation turbulent finite element model that can calculate values in the viscous sublayer. Implicit integration of the equations is used for determining the fluid velocity, pressure, turbulence, kinetic energy, and dissipation of turbulent kinetic energy. These values are calculated in the finite element nodes for each step of incremental-iterative procedure. Developed turbulent finite element model with the customized generation of finite element meshes is used for solving complex blood flow problems. FEM Analysis results for the artery geometry of the selected anonymous patient provides us with data about important hemodynamics parameters such are blood velocity field and wall shear stress. Cardiologists could use proposed tools and methods to supplement clinical investigation of the hemodynamic conditions inside bifurcation of arteries.

Continuous degeneration of muscle tissue, inflammatory processes and fibrosis characterized by a loss of muscle mass, formation of micro-scars, adipose tissue in the muscles and eventual muscle punctures are often signs of muscular dystrophies (dystrophinopathies). These neuromuscular diseases result from genetic mutations of a structural protein called dystrophin. The absence of functional dystrophin leads to the most common and severe form of muscular dystrophy, Duchenne muscular dystrophy (DMD). Typically, within one muscle bundle there are so-called fast and slow muscle fibers that shorten and lengthen at different speeds during muscle contraction. Using the multiscale muscle platform Mexie we evaluated how the lack of dystrophin affects the connective tissue deformation between these two types of muscle fibers. By adjusting the elasticity of extracellular matrix layer, we estimated the magnitude of the shear strain under unloaded and lightly loaded fiber contractions caused by differences in shortening velocities between fast and slow fibers. The simulations showed that without dystrophin large shear strains are generated causing local micro injury and inflammation leading to further muscle degeneration. The multiscale muscle modeling approach presented here could help accelerate understanding of DMD and lead to faster development of new drugs and treatments of patients.

Hypertrophic and Dilated Cardiomyopathies are caused by inherited mutations in sarcomeric proteins: Myosin (M), Troponin (Tn), Tropomyosin (Tm) and Myosin Binding Protein-C (MyBP-C). A quantitative understanding of how mutations change protein behaviour, and hence cardiac muscle contraction, and how adaptations to these changes result in disease, could accelerate the design of novel personalized treatments and therapeutics. Newly developed multiscale computational tools, tightly interlaced with multiple experiments, can enhance efforts to correct the problems associated with cardiomyopathies and prevent or more effectively manage the disease. Using these computational tools, we examined the effects of mutations in myosin and troponin on cardiac muscle contractility and overall heart functional behaviour. We also examined the effects of potential therapeutics that modulate protein interactions and cardiac muscle contractility.

In this paper, we will consider the forces in knee joints in football and futsal players during different types of jumps. We will consider two types of jumps: jumps with flywheel and jumps without flywheel. This study has two main aims. The first aim is to compare the knee joint forces in football players and futsal players during different types of jumps. The second aim is to determine the distribution of deformation and stress in menisci of the knee joints in football and futsal players. Professional futsal players performed jumps that were analyzed using a force plate and high-speed video camera system. For this purpose, a 3D model of the human knee joint was developed from medical scans. The 3D model of the human knee joint consisted of femur, fibula, tibia, articular cartilage, ligaments and menisci. Loads and material characteristics were adopted from the literature. The use of finite elements analysis enabled us to gain better understanding of the distribution of deformation and stress in specific parts of the knee joint, with the special focus on the menisci.

In this study, we will consider the forces in the knee joints of futsal players at the beginning and at the end of the training process focusing on two types of jumps: jumps without swing and jumps with swing. This study has two main objectives. The first objective is to compare the forces in the knee joint at the beginning and at the end of the training process in jumps without a swing and in jumps with a swing. The second objective is to show the distribution of deformations and stresses in the menisci of the knee joint at the beginning and at the end of the training process. Professional futsal players performed jumps that were analyzed using a force plate and high-speed video camera system. For this purpose, a 3D model of the human knee joint was developed from medical scans consisting of femur, fibula, tibia, articular cartilage, ligaments and menisci. Loads and material characteristics were adopted from the literature. Using finite element analysis, we were able to obtain a better insight into the distribution of deformation and stress in particular parts of the knee joint.

Cardiovascular diseases are the leading cause of death in the world with an incidence of about 30% of total mortality. It is a disease of the blood vessels of the heart that most often occurs due to the process of atherosclerosis. The process of atherosclerosis leads to narrowing of the coronary arteries, and thus to a reduced supply of blood or oxygen to the heart muscle. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. In this paper, we studied the values of the FFR, using numerical simulations, on the geometries obtained by reconstructing the angiogram images.

Colorectal cancer is one of the most common types of cancer and metastasis particular problem in anticancer treatment. Therefore, it is crucial to understand key steps in metastasis formation, such as loss of adherent junctions.$\mathbf{E}$-cadherin and$\beta$-catenin are proteins involved in cell-cell junctions in cancer cells. Present study aimed to explain changes in E-cadherin and$\beta$-catenin in two colorectal cancer cell lines after treatment with standard anticancer drug cisplatin, by using numerical modelling. The validity of the mathematical model was tested by experimental measurement of E-cadherin and$\beta$-catenin protein expression by immunofluorescent method. Our results shows that the numerical model of the Wnt pathway completely confirms experimental results for HCT-116 cells, while for SW-480 cells this model should be adjusted.

In this report we present basic steps in the 3D reconstruction process of DICOM images and application of our finite element (FE) numerical procedure for loose coupling solid-fluid interaction, to simulate a complete heartbeat cycle for a realistic model of the left heart side. Passive mechanical stresses are calculated using an orthotropic material model based on the experimental investigation of passive material properties of the myocardium, while active stresses are calculated using the Hunter material model. The basic equations for solid mechanics, fluid dynamics, and muscle activation are summarized and model applicability is illustrated on a complex realistic model which includes a left atrium, ventricle, mitral and aortic valves (which serve as fluid domain) coupled with solid wall with realistic fiber directions.

Diagnosing lumbar discus hernia is a challenging task, due to disc and vertebral variations in size, shape, quantity, and appearance. Medical history and physical examination, electrodiagnostic tests, and MRIs are all used by doctors to set a definitive diagnosis. A majority of the state-of-the-art methods are semi-automatic and require extra corrections to the solution or are extremely sensitive to changes in parameters. Based on literature review, there is a solid basis for implementation of machine learning-based methods for disc herniation detection in MRI images. An automated segmentation method of vertebrae and discs is proposed in this study as a first step towards a decision support system for discus hernia identification. Dataset consisted of 104 images in sagittal and 99 images in axial views. Optimized convolutional neural network U-net has demonstrated very high accuracy in segmentation. Additional result represents the calculated distance from the disc's center to the disc's edge points in axial images across 360°, which results in clearly different number of peaks for the healthy and diseased discs. Fully automated computer diagnostic system helps speed up the process of setting up adequate diagnosis and reducing human mistakes.

The aim of this work was to evaluate the impact of Bicuspid Aortic Valve (BAV), on displacements, Von Mises stress, shear stress and pressure distribution within the aortic root by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific geometry of dilated aortic root with BAV was reconstructed based on Computed Tomography (CT) scan images, in order to obtain the 3D finite element mesh. Two types of analyses: i) structural analysis and ii) computational fluid dynamics (CFD) were performed, with applied equivalent material characteristics of BAV and boundary conditions. The initial results for this single case, displacements and Von Mises stress distribution (for structural analysis), as well as shear stress and pressure distribution (for CFD analysis) were quantified concerning anatomical patient's structures. The regions of abnormal stresses on the aortic leaflets and annulus, with asymmetrically open bicuspid valve, were related to the increased pressures and shear stresses and analyzed for this patient-specific case. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the aortic root with BAV that is needed to achieve improvements in surgical repair techniques and presurgical planning.

Salivary gland ultrasonography (SGUS) represents a promising tool for diagnosing Primary Sjögren's syndrome (pSS), which is manifest with abnormalities in salivary glands (SG). In this study, we propose a fully automatic method for scoring SGs in SGUS images, which is the most important step towards SG the pSS diagnosis. A two-centric cohort included 600 images (150 patients) annotated by experienced clinicians. The aim of the study was to assess various deep learning classifiers (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, Inception_v3, and ResNet) for the purpose of the pSS scoring in SGUS. The training was performed using the ADAM optimizer and cross entropy loss function. Top performing algorithms were MobileNetV2, ResNet, and Dense-Net. The assessment showed that deep learning algorithms reached clinicians-level performances in the almost real-time. Considering that, the further work should be regarded towards evaluation on larger and international data sets with the goal to establish SGUS as an effective noninvasive pSS diagnostic tool.

During everyday activities cartilage experiences high loads, stresses, deformations, and contact forces. Sometimes, those activities can lead to permanent damage, such as focal lesions. Focal cartilage lesions have been associated with the progressive degeneration of the surrounding cartilage tissue. This paper aims to compare the mechanical response of the knee joint and femoral cartilage using finite element models during the stance phase of the gait cycle. Our model, developed from MRI scans, has been used to compare the mechanical response of the knee joint with healthy and damaged femoral cartilage. The location of the lesion was above the anterior section of the lateral meniscus. Comparison of the obtained results has shown that having a lesion in the previously mentioned location leads to a significantly higher peak Von Mises stress values.

It is important to investigate the state of the arteries in order to detect atherosclerotic plaques in the early stage and then treat them appropriately. One of the diagnostic techniques is the ultrasound (US) examination. In order to obtain a more detailed and comprehensive overview of the state of the patient's carotid artery, 3D reconstruction using the available 2D cross-sections can be performed. In this paper, deep learning is used for the automatic segmentation of US images, and this data is then used to reconstruct the 3D model of the patient-specific carotid artery. The validation of the proposed approach is performed by comparing two relevant clinical parameters for accessing the severity of vessel stenosis - the plaque length and the percentage of stenosis. Good validation results demonstrate that this method is capable of accurately performing segmentation of the lumen of carotid artery from US images and thus it can be a useful tool for assessing the state of the arteries in clinical diagnostics.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Salivary gland ultrasonography (SGUS) has shown good potential in the diagnosis of primary Sjögren's syndrome (pSS). However, a series of international studies have reported needs for improvements of the existing pSS scoring procedures in terms of inter/intra observer reliability before being established as standardized diagnostic tools. The present study aims to solve this problem by employing radiomics features and artificial intelligence (AI) algorithms to make the pSS scoring more objective and faster compared to human expert scoring. The assessment of AI algorithms was performed on a two-centric cohort, which included 600 SGUS images (150 patients) annotated using the original SGUS scoring system proposed in 1992 for pSS. For each image, we extracted 907 histogram-based and descriptive statistics features from segmented salivary glands. Optimal feature subsets were found using the genetic algorithm based wrapper approach. Among the considered algorithms (seven classifiers and five regressors), the best preforming was the multilayer perceptron (MLP) classifier (κ = 0.7). The MLP over-performed average score achieved by the clinicians (κ = 0.67) by the considerable margin, whereas its reliability was on the level of human intra-observer variability (κ = 0.71). The presented findings indicate that the continuously increasing HarmonicSS cohort will enable further advancements in AI-based pSS scoring methods by SGUS. In turn, this may establish SGUS as an effective noninvasive pSS diagnostic tool, with the final goal to supplement current diagnostic tests.

Pulmonary acinus represents the gas exchange unit which includes branches of the terminal bronchiole, alveolar ducts, alveolar sacs, alveoli and associated blood vessels. Over the past few decades, many results related to the fluid mechanics characterizing pulmonary acinus of the lungs have been reported. In order to describe a micromechanics in 3D acinar micro-architecture and airflow through it, 3D reconstruction of parenchyma with computational fluid dynamics plays an important role. For the reliable 3D model, precise image segmentation of the stacked 2D images is a necessary pre-step. However, in most cases this step is neglected and the classic threshold segmentation is applied. Convolutional neural networks proved to be very successful in image classification and object detection, and in the field of medical image segmentation U-Net architecture showed very good performance. In this paper, automatic pulmonary acinus lung field segmentation has been performed using U-Net based deep convolutional network. Our proposed model has been evaluated on the images of rat lungs imaged by synchrotron radiation-based X-ray tomographic microscopy (SRXTM). The experimental results show that our model outperforms the baseline models.

This paper presents preliminary results on non-invasive optical diagnosis of patient with adolescent idiopathic scoliosis (AIS) with recently developed tool ScolioSIM. This tool generates intrinsic and extrinsic indicators of the spinal deformity and 3D CAD (Computer-aided Design) deformity model based only on a digitalized dorsal surface of the examined patient. Our primary focus in this paper are absolute axial rotations (AAR) of each vertebra in AIS patients and their correlations. Clinical relevance of this research is high, as we intend to avoid traditional methods which rely on highly complicated reading and measuring of AARs on planar x-ray films, as these methods involve ionizing radiation and require highly experienced observers. Here we demonstrated a good correlation of AARs in 372 patients optically diagnosed, and currently we perform final verification of presented method and generic CAD model of the spine against 200 low-dose EOS scans of AIS patients.

One of the most common clinical treatments of arterial stenosis is the implantation of an endovascular prosthesis called a stent. Computer modeling represents a useful tool that enables the analysis of the complex behavior of the stent during implantation within a patient specific artery. The numerical model presented in this paper can simulate the expansion of multiple stents, as well as the interaction of the stents with the arterial wall and the behavior of the arterial wall due to the forces caused by the stents. Also, GPU principles are used during implementation, to enable the execution of simulations in real time. In this paper, two stents are deployed within patient-specific artery and the results of this simulation are presented. The numerical models that are originally applied in engineering are used in the presented numerical model, in order to create a useful tool that can be used in bio-medicine. This software can be used for a more detailed prediction of the shape of the stents and artery after implantation and thus this software can improve the techniques used for treatment of the arterial stenosis and pre-operative patient-specific planning.

In this report we implement our smeared model for electrical and calcium concentration field within a heart tissue and couple it to muscle mechanics. The basic equations are summarized and applicability of this model, coupling neural excitation and mechanics, is illustrated on one numerical example.

The main aim of this study is to develop an application which will be able to load DICOM images, segment a tumor on brain slices and create a three-dimensional model of the segmented tumor. Firstly, we present two algorithms - depth first search method (DFS) and active contours method, for brain tumor segmentation and compare their performance. Comparison of effectiveness of these two proposed algorithms included discussing parameters complexity of initial conditions that need to be set manually, accuracy of the segmented tumor surface area, and computational time. Described methodology was tested on computerized tomography (CT) medical images from 37 patients using two different algorithms. User-friendly application for tumor segmentation on two-dimensional images and three-dimensional visualization is developed in Java. Presented application and results can be used as assistance tools in cases of surgeries, but can also be helpful in brain tumor treatment.

Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper, we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis. In order to capture relationships between geometric parameters, blood density, dynamic viscosity and velocity, and WSS distribution of geometrically parameterized abdominal aortic aneurysm (AAA) and carotid bifurcation models, we proposed multivariate linear regression, multilayer perceptron neural network and Gaussian conditional random fields (GCRF). Results obtained in this paper show that machine learning approaches can successfully predict WSS distribution at different cardiac cycle time points. Even though all proposed methods showed high potential for WSS prediction, GCRF achieved the highest coefficient of determination (0.930-0.948 for AAA model and 0.946-0.954 for carotid bifurcation model) demonstrating benefits of accounting for spatial correlation. The proposed approach can be used as an alternative method for real time calculation of WSS distribution.

The main purpose of this study was to examine and compare the biomechanical characteristics of a healthy tricuspid aortic valve (TAV) and diseased bicuspid aortic valve (BAV). The patient-specific geometrical model of TAV was created based on computed tomography (CT) scan images. On the same model, two leaflets (left and right) were manually fused in order to create the BAV model (type 1). The finite element analysis was performed using the algorithms and numerical methods for structural analysis on computational meshes. Also, equivalent material characteristics and boundary conditions were applied. As the result, displacements and Von Mises stress distribution were computed concerning anatomical differences between TAV and BAV structures. In the case of TAV, leaflets were symmetrically and centrally open, while BAV analysis resulted with regions of increased stresses on the leaflets with elliptically open valve. The performed comparative computational analysis gave better insight into the biomechanics of healthy and malformed aortic root. It may contribute to monitoring of structural characteristics due to the difficulty of obtaining such characteristics in vitro or in vivo.

Atherosclerosis is characterized by dysfunction of endothelium, vasculitis and accumulation of lipid, cholesterol and cell elements inside blood vessel wall. Determination of plaque location and plaque volume for a specific patient is very important for prediction of atherosclerotic disease progression. In this study coupled computer modeling of atherosclerosis progression is analysed. Continuum approach assumed mass transport of LDL through the wall and the simplified inflammatory process coupled with three additional reaction-diffusion equations and lesion growth model in the intima. Discrete modeling used dissipative particle dynamics method which individual blood constituents (e.g., platelets, RBCs, white blood cells) treated as particle interaction. Coupled continuum and discrete model was investigated with real patient baseline and follow up study for right and left coronary arteries.

Aim of this study was to analyze how modifications in circulation chamber dimensions affect aerosol particle deposition in a Dry Powder Inhaler (DPI) Aerolizer®. Combining computational fluid dynamics (CFD), for simulation of fluid flow (air), with discrete phase model (DPM) for particles simulation, we can better understand particle dispersion within inhalers air flow field. Input in the simulation was 20mg of aerosol particles with initial velocity of 11,79166m/s. Dimension change influences maximum velocities, as well as percentage of deposited particles. Based on these information we were able to calculate the number of particles on the outlet and compare efficiency reduction when circulation chamber height increased. Knowledge obtained in this way can help in device performance optimization.

Hip replacement surgery is one of the most common and most successfully performed surgeries. Hip is an important joint in the human body that provides us with ability to perform different daily activities (walking, running, etc.). In this paper we have analyzed biomechanics of femoral bone with cementless hip prosthesis. The goal was to analyze stress distribution of the implant and femur bone. For numerical calculations of the stress distribution we have used finite element analysis. Presented results include von Mises stress distribution and Maximum Principal Stress distribution.

BACKGROUND: Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach. RESULTS: The proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments. CONCLUSION: We developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.

Benign paroxysmal positional vertigo (BPPV) is the most commonly diagnosed vertigo syndrome that affects 15% of older persons. BPPV is characterized by sudden attacks of dizziness and nausea triggered by changes in head orientation, and primarily afflicts the posterior canal. We are modeling human semicircular canals (SSC) which considers the morphology of the organs and the composition of the biological tissues and their viscoelastic and mechanical properties. The Navier-Stokes equations of balance of linear momentum and the continuity equation with application of Penalty method are used. For fluid-structure interaction problem we use loose coupling methodology with ALE (Arbitrary Lagrangian Eulerian) formulation. The tissue of SSC has nonlinear constitutive laws, leading to materially-nonlinear finite element formulation. The incremental-iterative equation is using for nonlinear wall tissue problem. Our results simulate many dynamics position of head and dynamic fluid parameters, shear stress, effective wall stress of membrane. This could help in better diagnostic and therapy process for BPPV disease.

This paper describes the use of artificial neural networks in predicting value and position maximal wall shear stress in aneurysm. For the purpose of neural network training, back propagation algorithm was used. Input data in the network are geometric parameters of aneurysm model. Obtained results indicate the possibility of a successful application of neural networks in the problems of predicting certain parameters of arteries. Future work relates to the creation of a web-based application that allows users to display the results.

In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms were successfully applied to a novel breast cancer data set of the Clinical Center of Kragujevac. The Naive Bayes classifier is selected as a model for prognosis of cancer survivability on the basis of the 5 years survival rate, while the Artificial Neural Network has achieved the best performance in prognosis of cancer recurrence. Selection of twenty attributes that are the most related to success of prognosis on survivability can give new insights into the set of prognostic factors which need to be observed by medical experts.

This paper presents computer analysis of how geometry of reconstructed gastrointestinal tract can influence outcome of Billroth II gastric resection. We performed three-dimensional computer simulation in order to predict duodenal stump blowout. For creation of initial three dimensional FE models of preoperative gastroduodenal region we used data from Multi Slice Computer Tomography (MSCT). Using the initial model we performed virtual gastric surgery. All post operational models were examined separately in order to find correlation between post operational geometry and pressure in duodenal stump. Data acquired by methodology presented in this study can be valuable to surgeons for prediction of suture dehiscence after gastric surgery.

The main purpose of this study is to examine how flow field in aortic dissection is affected by its geometry and flow condition. Two models of DeBakey type I aortic dissection, which involves the entire aorta, were analyzed. Patient-specific geometries were reconstructed, based on Computed tomography (CT) scan images, in order to obtain 3D finite element meshes. Computational fluid dynamics (CFD), which uses numeric methods and algorithms for the simulation of blood flow by solving the Navier-Stokes equations on computational meshes, enhances the understanding of disease progression. For that purpose, the major fluid dynamic parameters and indicators of disease progression, such as velocity field, pressure and shear stress, were computed and analyzed. The computed results showed higher velocities in the ascending aorta, the inlet and outlet tears and the iliac arteries, in case of both models. The pressure distribution showed high zones in the ascending aorta, while the shear stress distribution showed low zones in the aneurysm part, in case of both models. In summary, the presented study can be extended to a larger patient group in a longitudinal study with the goal to determine the potential value of CFD simulations in prediction of aneurysmal growth and rupture.

One of the approaches that could be used for cancer treatment is electroporation. This is a relatively new technique and thus its effect on various cancer cell types should be analyzed in detail. In this paper numerical simulations are used, in order to model the behavior of cells after electroporation. Fitting procedure was used for estimation of the parameters of the computer model. This model enables continuous tracking of changes in cell viability and provides some quantitative information about the effect of electric field (change in proliferation and death rate, oxygen consumption etc.). The accuracy of the model is validated using experimental data.

Investigation of the motion of deformable particles immersed in fluid is clinically very relevant because it can help to improve treatment planning, diagnostics of disease, design of efficient terapeutical procedures, analysis of drug transport, etc. Experimental investigation of these phenomena is difficult and expensive and, therefore, numerical simulations can contribute to the acquisition of a lot of new and useful information. In this paper, a numerical model that simulates solid-fluid interaction is presented and used to simulate the motion of red blood cells and spherical particles through a fluid domain. A comparison with experimental results and other results presented in literature is performed. The good accuracy of the model demonstrates that this method has a great potential for simulating phenomena happening within complex geometric domains, such as microfluidic chips for cancer cell separation.

One of the key challenges in osteochondral tissue engineering is to achieve mechanical properties in the engineered tissue that are equivalent to the native tissue. Detailed knowledge of the mechanotransduction pathways occuring in the native tissue is necessary before manipulation towards the aimed properties in the engineered tissue. Purinergic (ATP-mediated) signaling is proving to be one of the main mechanisms how the chondrogenic cells sense and respond to the mechanical stimulation. In this study we performed experiments to evaluate the mechanosensitive purinergic response of chondrocytes and chondrogenic mesenchymal stem cells and we further developed a mathematical model showing ATP release changes in loaded vs. unloaded cell constructs over time. Such model can be of value in determining the potential for pharmacological manipulation of the purinergic mechanotransduction in the engineered osteochondral tissues.

Human hearing system in general, and particularly the cochlea, is very interesting for investigation. The most important reason for it is hearing loss - a health problem that affects a large part of the world's human population. The highest percentage of people with hearing problems are older people, but the problem also occurs in newborns. Experimental research in this area provides some information about the level of hearing loss. Therefore, it is very useful to have a numerical model of the hearing system that can significantly contribute to the understanding of the origin of the mentioned health problem. Two numerical models are developed to investigate hearing problems: passive 3D cochlea model and 2D cochlea cross-section model. Those models are weakly coupled in order to make an active cochlea model [1].

In order to improve locoregional tumor control obtainable with radiation therapy (RT) alone, many approaches have been used. In the past decade, focus of interests was on metal-enhanced RT as metal nanoparticles (NP) held promise in simulation studies as well as both in vitro and in vivo radiosensitizing investigations. Gold NPs have predominantly been studied showing unequivocally great potential for radiosensitization. In clinical scenarios, however, it is presently limited due to less sensitization when megavoltage beams (mostly used in clinic) are compared to kilovoltage beams. A number of other metal NPs are also under investigation as well as combination of RT-NP with either classical chemotherapy or targeted agents and other cancer treatments (hyperthermia, photodynamic therapy). Ultimately, more clinical studies are needed to verify these promising results in clinical setting.

The role of intraluminal thrombus (ILT) has special attention in these studies. One of the papers showed that asymmetrical intraluminal thrombus deposition (ATDI) has an important role in growth of the AAA. The aim of our study was to assess the asymmetrical thrombus deposition index in ruptured and symptomatic aneurysms. We collected data for 33 aneurysms, 21 (63.63%) asymptomatic and 12 (33.37%) ruptured or symptomatic. Asymmetrical thrombus deposition index (ATDI) was measured by Onis DICOM viewer software. Also, lumen's geometrical centre (LGC) was defined and ATDI was considered positive when the LGC was laid on the posterior section of the sac (meaning dominant anterior ILT distribution) and negative when it was laid on the anterior section (meaning dominant posterior ILT distribution). Maximum aneurysm diameter was 63.4mm in average (50-100mm, SD=12.89); 59.8mm in asymptomatic and 71.16mm in symptomatic or ruptured aneurysm (p=0.012). The absolute value of asymmetric thrombus deposition index was significantly higher in symptomatic/ruptured compared to asymptomatic aneurysm, 0.54 and 0.33, respectively (p=0.041), while there was no difference in frequency of positive or negative thrombus deposition (p=0.261). There was no significant correlation between maximal aneurysm size and absolute value of ATDI (p=0.505). Values of thrombus deposition index are correlating with the development of symptomatology or rupture of the AAA. This variable should be included in much wider mathematical rupture prediction model in order to have more accurate rupture risk assessment.

In this paper the methodology of vertical jump analysis is presented. Measured results of vertical force during jump are presented. Six subjects (members of "Red star" football club) perform different types of jump (flywheel jump, jump without flywheel, and jump with landing on the left and right foot while vertical ground reaction force is measured using a force plate. One axial load cell force sensor is also used. The measure value of force and position of a body part is used together with finite element method simulation in order to obtain von Mises stress distribution on the tibia, femur and cartilage in the knee joint. The average value of von Mises stress has a significant impact on the injuries and condition of the knee cartilage.

Image filtering is one of the most common and important tasks in image processing applications. In this paper, image processing using a mean filtering algorithm combined with thresholding and binarization algorithms for the 3D visualization and analysis of murine lungs is explained. These algorithms are then mapped on the Maxler's MAX2336B Dataflow Engine (DFE) to significantly increase calculation speed. Several different DFE configurations were tested and each yielded different performance characteristics. Optimal algorithm calculation speed was up to 30 fold baseline calculation speed.

Cochlea is a part of the inner ear and it has complex anatomy and function. The proper functioning of the cochlea includes the generation of a traveling wave along the basilar membrane, which leads to depolarization of hair cells in the Organ of Corti and subsequent auditory nerve excitation (mechanoelectrical transduction). To represent the behavior of the organ of Corti, electro-mechanical cochlea model needs to be developed. This paper presents a simplified electro-mechanical state space model of the cochlea.

Hybrid imaging combining CT and SPECT is becoming a state of the art nuclear medicine technique. Advantages of hybrid imaging are improved quality of the images using CT data for attenuation correction based on true transmission density data in an individual patient, and CT and SPECT fusion images providing accurate localization of the tracer uptake. Accurate localization of increased tracer uptake is very important especially in the diagnostic of tumors. For this purpose a software developed for generating 3D models from standard DICOM images, obtained from CT and SPECT, and then merging these 3D objects, provides us with the exact location of sentinels nodes. This is certainly very helpful for a surgeon performing biopsy. The ability of SPECT/CT to improve diagnostic accuracy, especially specificity, has a great potential in further development of nuclear medicine techniques in evaluation of tumors.

The aim of this study is to determine the internal reliability volume of movements of top players during matches measured using a software system Tracking Motion. The research was conducted on a sample of 23 players, which were recorded during six official matches. The variables were administered to assess the internal variability of movement of top players during the match measured by Tracking Motion software system (BioIRC, Kragujevac, Serbia). Parameters' movement structure were analyzed using descriptive statistical method. The reliability of monitored variables was examined using linear regression analysis. The results for the reliability of motion analysis of elite soccer players during match measured by the Tracking Motion software system are shown.

Atherosclerosis is a medical condition becoming the number one cause of death worldwide. For this reason, any developement that may help physicians in early diagnostic and selection of the most appropriate treatment strategy is of great importance. In this paper we describe three-dimensional computer model of plaque formation and development for human coronary artery. In order to validate proposed model we used ten specific patients from CT study belonging to one of the following groups: (1) de-novo group - patients with new formed plaques, (2) old-lesions group - patients with plaques with progression and (3) control group - patients with plaques without progression. Plaque volume progression is fitted by using two time points for baseline and follow up. Results obtained within this study indicate high potential of this model to be used in clinical practice, thus assisting physicians by providing them valuable information about future disease progression.

In this study the authors present the method for determination of exposure dose on human head during computer tomography (CT) scanning procedure. The method is based on the use of the feed-forward neural network (FFNN) model to predict the exposure dose on human head. The neural network with Levenberg-Marquardt learning is constructed. The training data are obtained using the Monte Carlo method simulation. The simulation is performed by generating random numbers for determination of photon direction and for quantification of interaction between X-ray photon and head tissue. Spectra of photon energy is used for 3DCT scanner, X-ray tube Model XRS-125-7K-P. The FFNN predicted values are in accordance with the values obtained by the simulation with correlation coefficient around 0.99.

This paper describes the application of adaptive neuro-fuzzy inference architecture for supporting the diagnosis of lumbar disc herniation. The fuzzy system has been trained with the backpropagation gradient descent method in combination with the least squares method. A total of 38 patients have been divided into training and testing data sets. The performance of the fuzzy model has been evaluated in terms of classification accuracies and the results of the simulation confirmed that the proposed fuzzy approach has potential in supporting the diagnosis of lumbar disc herniation.

The finite element method is increasingly used in the analysis of blood flow through blood vessels. Our work represents the research of blood flow through a cerebral aneurysm. The current work describes the blood flow in 4 patient-specific models of saccular aneurysms. They are located in the region of the anterior and posterior circulation of the circle of Willis. The unstructed grids are constructed from segmented images. Using three-dimensional continuity and momentum equations for unsteady laminar flow and realistic pulsatile flow conditions, we determined the wall shear stress (WSS), pressure and drag forces that acting on the wall of the blood vessel. It is known that wall shear stress (WSS) play an important role in initiation, growth and rupture of cerebral aneurysm, so, determination of the forces that acting in this region helps with understanding aneurysms better.

The most common type of heart disease that affects millions of people worldwide is coronary heart disease (coronary artery disease). It is caused by a narrowing or blocking of the arteries due to plaque which restricts blood flow, and reduces the amount of oxygen to the heart [1]. Several tools are used that aid physicians in the treatment of the disease. Angiogram, which represents an X-ray examination of the blood vessels in the heart, is traditional tool. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. FFR is measured by a pressure sensor guidewire [2]. In this paper, the mathematical model for measuring FFR is derived. This model helps to measure values of FFR, by noninvasive methods, only by using reconstructed geometry of coronary arteries with stenosis.

In this paper we present a novel approach in multi-scale muscle modeling based on finite element method and Huxley crossbridge kinetics model. In order to determine the mechanical response of a muscle, we implement basic mechanical principles of motion of deformable bodies using finite element method. Constitutive properties of muscle are defined by the number of molecular interconnections between the myosin and actin filaments. To account for these effects, we used Huxley's micro model based on sliding filament theory to calculate muscle active forces and instantaneous stiffnesses in FE integration points. In order to run these computationally expensive simulations we have also developed a special parallelization strategy which gives speedup of two orders of magnitude. Results obtained using presented multi-scale model are compared to those obtained by Hill's phenomenological model.

In this paper we present a method for brain tumor segmentation and assess its performance discussing parameters - complexity of initial conditions that need to be set manually, tumor surface area recognition, and computational time. The methodology includes performing segmentation on computerized tomography (CT) medical images from 37 patients. Furthermore, one approach to user friendly two- and three-dimensional tumor visualization is proposed. The results obtained in this paper can be new paradigm in the assessment of optimal approach trajectory to brain tumor in surgical operation.

Smoothed Particle Hydrodynamics is meshless numerical method which is based on continuum mechanics approach, capable of analyzing stresses in both solids and fluids as well as stresses that are result of solid-fluid interaction, with very versatile applications, and yet it' is not sufficiently implemented in biomechanics due to difficulties of node grid generation from complex shape objects. This paper presents multiblock procedure for generation of pseudo-particles which are used in Smoothed Particle Hydrodynamics for representation of discretized parts of analyzed continua. This procedure enables creation of evenly sized pseudo-particles even for the very irregular shaped object such are organs, bones or blood vessels which are analyzed in biomechanics.

Assessment of the risks associated with bone injures is nontrivial because fragility of human bones is varying with aging. Since only a limited number of experiments have been performed on the specimens from human donors, there is limited number of fracture resistance curves available in literature. This study proposes a decision support system for the assessment of bone stress intensity factor by using artificial neural networks (ANN). The procedure estimates stress intensity factor according to patient's age and diagnosed crack length. ANN was trained using the experimental data available in literature. The automated training of ANN was performed using evolutionary assembled Artificial Neural Networks. The obtained results showed good correlation with the experimental data, with potential for further improvements and applications.

A three dimensional biomechanical model of the human knee joint was developed. The model was created from MRI scans and includes: bones, menisci, articular cartilage and relevant ligaments (posterior cruciate ligament, lateral collateral ligament and medial collateral ligament). The purpose of this study was to compare the stress distribution on the human knee joint in two situations. The first situation includes the rupture of anterior cruciate ligament (ACL) while the second situation includes the ACL rupture and the condition after medial meniscectomy is performed. We have used the finite element model of human knee joint to measure stress when person is standing on one foot. The finite element analysis can provide better insight at the situation in the knee joint when having anterior cruciate ligament and meniscus injury.

In this study we investigated the hot caloric test response in the three semicircular canals using coupled fluid flow, natural convection and fluid-structure interaction with the finite element method. We demonstrated that the temperature distribution of the horizontal canal duct is more dominant and a longer period of irrigation time is required in order to stimulate the two other vertical canals. Our results also show shear stress and force distribution from end lymph flow during natural convection. Future studies are necessary for validation of the presented computer model with clinical measurements.

There is a strong interest of investigation of Extremely Low Frequency (ELF) ElectroMagnetic (EM) fields in the clinic. In this study we investigated experimentally in-vitro and in-sillico with computer simulation influence of 50 Hz EM field at three different cancer cell lines: breast cancer MDA-MB-231 and colon cancer SW-480 and HCT-116. Computer reaction-diffusion model with the net rate of cell proliferation and effect of electromagnetic field in time was developed. The fitting procedure for estimation of the computer model parameters was implemented. Experimental and computer model data have shown good comparison. These findings can open a new avenue for better controlling the growth of cancer cells at specific frequencies without affecting normal tissues, which may have a great influence in clinical oncology.

Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontology-based biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

The cochlea is a very interesting part of the body. There are several investigations of experiments on the real cochlea and mathematical models. The cochlea works on the basis of a vibrating system. SIFEM project focuses on the development the multi-scale modelling of the inner-ear with regard to the sensorineural hearing loss. In this study we focused on the finite element model of the cochlea. The first approximation is straight box model where both domain basilar membrane and surrounding fluid are modeled. Fluid-structure interaction problem was implemented. The basilar membrane was modeled as structural plate with 3D brick finite element and fluid domain around the basilar membrane was modeled as full 3D Navier-Stokes equations. ALE formulation was employed for fluid domain and mesh moving algorithm for motion of the membrane and fluid mesh. The results for different frequencies for 3D box and spiral model are presented. It can be observed that viscous fluid allow a sharper response of the membrane, because the viscous fluid would quickly damp out the vibratory motion.

Aneurysm rupture is a biomechanical phenomenon that occurs when the mechanical stress acting on the inner wall exceeds the failure strength of the diseased aortic tissue. Besides numerous advantages in surgical and anaesthesiological management, emergency procedure leads to fatal outcome in 20-50% of those who reach hospital. Prediction of influence of dynamic blood flow on natural history of aneurysmatic disease and outcome of therapeutic procedures could contribute to treatment strategy and results. In this study we presented experimental design for estimation of the material property of real human aorta tissue from bubble inflation test. Then we investigated fluid-structure interaction of pulsatile blood flow in the specific patient three-dimensional model of abdominal aortic aneurysms (AAAs). Numerical predictions of blood flow patterns and nonlinear wall stresses in AAAs are performed in compliant wall anisotropic model using the finite element method. These computational procedures together with experimental determination of the nonlinear material property could provide us more accurate assessment of aneurysm rupture risk.

One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.

In this study, we present an innovative model for plaque growth utilizing a 3-Dimensional (3D) left coronary arterial tree reconstructed from computed tomographic (CT) data. The proposed model takes into consideration not only the effect of the local hemodynamic factors but also major biological processes such as the low density lipoprotein (LDL) and high density lipoprotein (HDL) transport, the macrophages recruitment and the foam cells formation. The endothelial membrane is considered semi-permeable and endothelial shear stress dependent, while its permeability is modeled using the Kedem-Katscalsky equations. Patient specific biological data are used for the accurate modeling of plaque formation process. The finite element method (FEM) is employed for the solution of the system of partial differential equations. The results of the simulation are compared to the plaque progression in a follow-up CT examination performed three years after the initial investigation. The results show that the proposed model can be used to predict regions prone for plaque development of progression.

One of the most common causes of human death is stroke, which can be caused by carotid bifurcation stenosis. In our work, we aim at proposing a prototype of a medical expert system that could significantly aid medical experts to detect hemodynamic abnormalities (increased artery wall shear stress). Based on the acquired simulated data, we apply several methodologies for1) predicting magnitudes and locations of maximum wall shear stress in the artery, 2) estimating reliability of computed predictions, and 3) providing user-friendly explanation of the model's decision. The obtained results indicate that the evaluated methodologies can provide a useful tool for the given problem domain.

Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in arteries. It is characterized by dysfunction of endothelium and vasculitis, and accumulation of lipid, cholesterol, and cell elements inside blood vessel wall. In this study, a continuum-based approach for plaque formation and development in 3-D is presented. The blood flow is simulated by the 3-D Navier-Stokes equations, together with the continuity equation while low-density lipoprotein (LDL) transport in lumen of the vessel is coupled with Kedem-Katchalsky equations. The inflammatory process was solved using three additional reaction-diffusion partial differential equations. Transport of labeled LDL was fitted with our experiment on the rabbit animal model. Matching with histological data for LDL localization was achieved. Also, 3-D model of the straight artery with initial mild constriction of 30% plaque for formation and development is presented.

Computational fluid dynamics methods based on in vivo 3-D vessel reconstructions have recently been identified the influence of wall shear stress on endothelial cells as well as on vascular smooth muscle cells, resulting in different events such as flow mediated vasodilatation, atherosclerosis, and vascular remodeling. Development of image-based modeling technologies for simulating patient-specific local blood flows is introducing a novel approach to risk prediction for coronary plaque growth and progression. In this study, we developed 3-D model of plaque formation and progression that was tested in a set of patients who underwent coronary computed tomography angiography (CTA) for anginal symptoms. The 3-D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The low density lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full 3-D model was created. It includes blood flow and LDL concentration, as well as plaque formation and progression. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed by repetition of CTA, six months after the baseline evaluation. Besides the low values of local shear stress, plaque characteristics, risk profile, pattern of circulating adhesion molecules, and reduced coronary flow reserve at baseline appeared to affect plaque progression toward flow-limiting lesions at follow-up evaluation. Although preliminary, our multidisciplinary approach to a “personalized” prediction of coronary plaque progression suggests that incorporation in atherosclerotic models of systemic and local hemodynamic features may better predict evolution of plaques in coronary artery disease stable patients.

Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.