Assessing the knot in a noose position by thyrohyoid and cervical spine fracture patterns in suicidal hangings using machine learning algorithms: A new insight into old dilemmas.

Hanging is one of the most common suicide methods worldwide. Neck injuries that occur upon such neck compression - fractures of the thyrohyoid complex and cervical spine, occupy forensic pathologists for a long time. However, research failed to identify particular patterns of these injuries corresponding to the force distribution a ligature applies to the neck: the issue of reconstructing the knot in a noose position persists. So far, machine learning (ML) models were not utilized to classify knot positions and reconstruct this event. We conducted a single-institutional, retrospective study on 1235 autopsy cases of suicidal hanging, developed several ML models, and assessed their classification performance in a stepwise manner to discriminate between: 1. typical ('posterior) and atypical ('anterior' and 'lateral') hangings, 2. anterior and lateral hangings, and 3. left and right lateral hangings. The variable coding was based on the presence/absence of fractures of greater hyoid bone horns (GHH), superior thyroid cartilage horns (STH), and cervical spine. Subject age was considered. The models' parameters were optimized by the Genetic Algorithm. The accuracy of ML models in the first step was very modest (c. 60%) but increased subsequently: Multilayer Perceptron - Artificial Neural Network and k-Nearest Neighbors performed excellently discriminating between left and right lateral hangings (accuracy 91.8% and 90.6%, respectively). The latter is of great importance for clarifying probable hanging fracture biomechanics. Alongside the conventional inferential statistical analysis we performed, our results further indicate the association of the knot position with ipsilateral GHH and contralateral STH fractures in lateral hangings. Moreover, odds for unilateral GHH fracture, simultaneous GHH and STH fractures, and cervical spine fracture were significantly higher in atypical ('anterior' and 'lateral') hangings, compared to typical ('posterior') hangings.

Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators' Psychological Status and Pain Syndromes.

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators' psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks-concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators' poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).

The compliance of head-mounted industrial PPE by using deep learning object detectors.

The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author's collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)-which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types-while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research.

The industry increasingly insists on academic cooperation to solve the identified problems such as workers' performance, wellbeing, job satisfaction, and injuries. It causes an unsafe and unpleasant working environment that directly impacts the quality of the product, workers' productivity, and effectiveness. This study aimed to give a specialized solution for tests and explore possible solutions to the given problem in neuroergonomics and human-robot interaction. The designed modular and adaptive laboratory model of the industrial assembly workstation represents the laboratory infrastructure for conducting advanced research in the field of ergonomics, neuroergonomics, and human-robot interaction. It meets the operator's anatomical, anthropometric, physiological, and biomechanical characteristics. Comparing standard, ergonomic, guided, and collaborative work will be possible based on workstation construction and integrated elements. These possibilities allow the industry to try, analyze, and get answers for an identified problem, the condition, habits, and behavior of operators in the workplace. The set-up includes a workstation with an industry work chair, a Poka-Yoke system, adequate lighting, an audio 5.0 system, containers with parts and tools, EEG devices (a cap and smartfones), an EMG device, touchscreen PC screen, and collaborative robot. The first phase of the neuroergonomic study was performed according to the most common industry tasks defined as manual, monotonous, and repetitive activities. Participants have a task to assemble the developed prototype model of an industrial product using prepared parts and elements, and instructed by the installed touchscreen PC. In the beginning, the participant gets all the necessary information about the experiment and gets 15 min of practice. After the introductory part, the EEG device is mounted and prepared for recording. The experiment starts with relaxing music for 5 min. The whole experiment lasts two sessions per 60 min each, with a 15 min break between the sessions. Based on the first experiments, it is possible to develop, construct, and conduct complex experiments for industrial purposes to improve the physical, cognitive, and organizational aspects and increase workers' productivity, efficiency, and effectiveness. It has highlighted the possibility of applying modular and adaptive ergonomic research laboratory experimental set-up to transform standard workplaces into the workplaces of the future.

Virtual ABI: A computationally derived ABI index for noninvasive assessment of femoro-popliteal bypass surgery outcome.

BACKGROUND AND OBJECTIVE: Peripheral arterial disease of the lower limbs, which affects 12-14% of the population, is often treated by bypassing a blocked portion of the vessel. Due to the limited ability of clinicians to predict the outcome of a selected bypass strategy, the five-year graft occlusion ranges from 50% to 90%, with a 20% risk of amputation in the first 5 years after the surgery. The aim of this study was to develop a computational procedure that could enable surgeons to reduce negative effects by assessing patient-specific response to the available surgical strategies. METHODS: The Virtual ABI assumes patient-specific finite element modeling of patients' hemodynamics from routinely acquired medical scans of lower limbs. The key contribution of this study is a novel approach for prescribing boundary conditions, which combines noninvasive preoperative measurements and results of numerical simulations. RESULTS: The validation performed on six follow-up cases indicated high reliability of the Virtual ABI, since the correlation with the experimentally measured values of ankle-brachial index was R² = 0.9485. CONCLUSION: The initial validation showed that the proposed Virtual ABI is a noninvasive procedure that could assist clinicians to find an optimal strategy for treating a particular patient by varying bypass length, choosing adequate diameter, position and shape.

OpenMandible: An open-source framework for highly realistic numerical modelling of lower mandible physiology.

OBJECTIVE: Computer modeling of lower mandible physiology remains challenging because prescribing realistic material characteristics and boundary conditions from medical scans requires advanced equipment and skill sets. The objective of this study is to provide a framework that could reduce simplifications made and inconsistency (in terms of geometry, materials, and boundary conditions) among further studies on the topic. METHODS: The OpenMandible framework offers: 1) the first publicly available multiscale model of the mandible developed by combining cone beam computerized tomography (CBCT) and µCT imaging modalities, and 2) a C++ software tool for the generation of simulation-ready models (tet4 and hex8 elements). In addition to the application of conventional (Neumann and Dirichlet) boundary conditions, OpenMandible introduces a novel geodesic wave propagation - based approach for incorporating orthotropic micromechanical characteristics of cortical bone, and a unique algorithm for modeling muscles as uniformly directed vectors. The base intact model includes the mandible (spongy and compact bone), 14 teeth (comprising dentin, enamel, periodontal ligament, and pulp), simplified temporomandibular joints, and masticatory muscles (masseter, temporalis, medial, and lateral pterygoid). RESULTS: The complete source code, executables, showcases, and sample data are freely available on the public repository: https://github.com/ArsoVukicevic/OpenMandible. It has been demonstrated that by slightly editing the baseline model, one can study different "virtual" treatments or diseases, including tooth restoration, placement of implants, mandible bone degradation, and others. SIGNIFICANCE: OpenMandible eases the community to undertake a broad range of studies on the topic, while increasing their consistency and reproducibility. At the same time, the needs for dedicated equipment and skills for developing realistic simulation models are significantly reduced.

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary glands (SGs), including primary Sjögren's syndrome (pSS). At present, the major obstacle for establishing SUGS as a standardized tool for pSS diagnosis is its low inter/intra observer reliability. The aim of this study was to address this problem by proposing a robust deep learning-based solution for the automated segmentation of SGUS images. For these purposes, four architectures were considered: a fully convolutional neural network, fully convolutional "DenseNets" (FCN-DenseNet) network, U-Net, and LinkNet. During the course of the study, the growing HarmonicSS cohort included 1184 annotated SGUS images. Accordingly, the algorithms were trained using a transfer learning approach. With regard to the intersection-over-union (IoU), the top-performing FCN-DenseNet (IoU = 0.85) network showed a considerable margin above the inter-observer agreement (IoU = 0.76) and slightly above the intra-observer agreement (IoU = 0.84) between clinical experts. Considering its accuracy and speed (24.5 frames per second), it was concluded that the FCN-DenseNet could have wider applications in clinical practice. Further work on the topic will consider the integration of methods for pSS scoring, with the end goal of establishing SGUS as an effective noninvasive pSS diagnostic tool. To aid this progress, we created inference (frozen models) files for the developed models, and made them publicly available.

Salivary Gland Ultrasonography in Sjögren's Syndrome: A European Multicenter Reliability Exercise for the HarmonicSS Project.

Objectives: Salivary gland ultrasonography (SGUS) is increasingly applied for the management of primary Sjögren's syndrome (pSS). This study aims to: (i) compare the reliability between two SGUS scores; (ii) test the reliability among sonographers with different levels of experience. Methods: In the reliability exercise, two four-grade semi-quantitative SGUS scoring systems, namely De Vita et al. and OMERACT, were tested. The sonographers involved in work-package 7 of the HarmonicSS project from nine countries in Europe were invited to participate. Different levels of sonographers were identified on the basis of their SGUS experience and of the knowledge of the tested scores. A dedicated atlas was used as support for SGUS scoring. Results: Twenty sonographers participated in the two rounds of the reliability exercise. The intra-rater reliability for both scores was almost perfect, with a Light's kappa of 0.86 for the De Vita et al. score and 0.87 for the OMERACT score. The inter-rater reliability for the De Vita et al. and the OMERACT score was substantial with Light's Kappa of 0.75 and 0.77, respectively. Furthermore, no significant difference was noticed among sonographers with different levels of experience. Conclusion: The two tested SGUS scores are reliable for the evaluation of major salivary glands in pSS, and even less-expert sonographers could be reliable if adequately instructed.

Radiomics-Based Assessment of Primary Sjögren's Syndrome From Salivary Gland Ultrasonography Images.

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.

Imaging in primary Sjögren's syndrome: the 'obsolete and the new'.

Primary Sjögren's syndrome (pSS) is a complex systemic autoimmune disease primarily characterised by a focal chronic inflammation of glandular parenchyma, with chronic and persistent involvement of major salivary gland remaining a key element of the disease. Indeed, classification criteria proposed for pSS have always included items for histological and/or imaging salivary gland assessment. Over time, the approach to the definition of glandular involvement in pSS is constantly evolving. In this review we will therefore illustrate the state of the art of imaging techniques in pSS, focusing on conventional and novel modalities and discussing their advantages, drawbacks and possible future developments.

Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression.

Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R2 = 0.82 vs. R2 = 0.76) and Toughness threshold with ageing (R2 = 0.73 vs. R2 = 0.66).

Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images.

Despite its two-dimensional nature, X-ray angiography (XRA) has served as the gold standard imaging technique in the interventional cardiology for over five decades. Accordingly, demands for tools that could increase efficiency of the XRA procedure for the quantitative analysis of coronary arteries (CA) are constantly increasing. The aim of this study was to propose a novel procedure for three-dimensional modeling of CA from uncalibrated XRA projections. A comprehensive mathematical model of the image formation was developed and used with a robust genetic algorithm optimizer to determine the calibration parameters across XRA views. The frames correspondences between XRA acquisitions were found using a partial-matching approach. Using the same matching method, an efficient procedure for vessel centerline reconstruction was developed. Finally, the problem of meshing complex CA trees was simplified to independent reconstruction and meshing of connected branches using the proposed nonuniform rational B-spline (NURBS)-based method. Because it enables structured quadrilateral and hexahedral meshing, our method is suitable for the subsequent computational modelling of CA physiology (i.e. coronary blood flow, fractional flow reverse, virtual stenting and plaque progression). Extensive validations using digital, physical, and clinical datasets showed competitive performances and potential for further application on a wider scale.

Trauma of the Frontal Region Is Influenced by the Volume of Frontal Sinuses. A Finite Element Study.

Anatomy of frontal sinuses varies individually, from differences in volume and shape to a rare case when the sinuses are absent. However, there are scarce data related to influence of these variations on impact generated fracture pattern. Therefore, the aim of this study was to analyse the influence of frontal sinus volume on the stress distribution and fracture pattern in the frontal region. The study included four representative Finite Element models of the skull. Reference model was built on the basis of computed tomography scans of a human head with normally developed frontal sinuses. By modifying the reference model, three additional models were generated: a model without sinuses, with hypoplasic, and with hyperplasic sinuses. A 7.7 kN force was applied perpendicularly to the forehead of each model, in order to simulate a frontal impact. The results demonstrated that the distribution of impact stress in frontal region depends on the frontal sinus volume. The anterior sinus wall showed the highest fragility in case with hyperplasic sinuses, whereas posterior wall/inner plate showed more fragility in cases with hypoplasic and undeveloped sinuses. Well-developed frontal sinuses might, through absorption of the impact energy by anterior wall, protect the posterior wall and intracranial contents.

Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems.

Impact of the lower third molar presence and position on the fragility of mandibular angle and condyle: A Three-dimensional finite element study.

The aim of the present study was to investigate the influences of the presence and position of a lower third molar (M3) on the fragility of mandibular angle and condyle, using finite element analysis. From computed tomographic scans of a human mandible with normally erupted M3, two additional virtual models were generated: a mandibular model with partially impacted M3 and a model without M3. Two cases of impact were considered: a frontal and a lateral blow. The results are based on the chromatic analysis of the distributed von Mises and principal stresses, and calculation of their failure indices. In the frontal blow, the angle region showed the highest stress in the case with partially impacted M3, and the condylar region in the case without M3. Compressive stresses were dominant but caused no failure. Tensile stresses were recorded in the retromolar areas, but caused failure only in the case with partially impacted M3. In the lateral blow, the stress concentrated at the point of impact, in the ipsilateral and contralateral angle and condylar regions. The highest stresses were recorded in the case with partially impacted M3. Tensile stresses caused the failure on the ipsilateral side, whereas compressive stresses on the contralateral side.

Influence of dental restorations and mastication loadings on dentine fatigue behaviour: Image-based modelling approach.

OBJECTIVES: The aim of this study was to use Finite Element Analysis (FEA) to estimate the influence of various mastication loads and different tooth treatments (composite restoration and endodontic treatment) on dentine fatigue. The analysis of fatigue behaviour of human dentine in intact and composite restored teeth with root-canal-treatment using FEA and fatigue theory was performed. METHODS: Dentine fatigue behaviour was analysed in three virtual models: intact, composite-restored and endodontically-treated tooth. Volumetric change during the polymerization of composite was modelled by thermal expansion in a heat transfer analysis. Low and high shrinkage stresses were obtained by varying the linear shrinkage of composite. Mastication forces were applied occlusally with the load of 100, 150 and 200N. Assuming one million cycles, Fatigue Failure Index (FFI) was determined using Goodman's criterion while residual fatigue lifetime assessment was performed using Paris-power law. RESULTS: The analysis of the Goodman diagram gave both maximal allowed crack size and maximal number of cycles for the given stress ratio. The size of cracks was measured on virtual models. For the given conditions, fatigue-failure is not likely to happen neither in the intact tooth nor in treated teeth with low shrinkage stress. In the cases of high shrinkage stress, crack length was much larger than the maximal allowed crack and failure occurred with 150 and 200N loads. The maximal allowed crack size was slightly lower in the tooth with root canal treatment which induced somewhat higher FFI than in the case of tooth with only composite restoration. CONCLUSIONS: Main factors that lead to dentine fatigue are levels of occlusal load and polymerization stress. However, root canal treatment has small influence on dentine fatigue. CLINICAL SIGNIFICANCE: The methodology proposed in this study provides a new insight into the fatigue behaviour of teeth after dental treatments. Furthermore, it estimates maximal allowed crack size and maximal number of cycles for a specific case.

Finite element analysis of uncommonly large renal arteriovenous malformation--adjacent renal cyst complex.

BACKGROUND: Renal arteriovenous malformation (RAVM) represents abnormal communication between the intrarenal arterial and venous system. The purpose of this study was to investigate hemodynamics and biomechanics quantities which may influence the instability of RAVM and imply clinical complications. METHODS: A detailed 3D reconstruction of RAVM was obtained from the patient CT scans, aortic inlet flow was measured by color-flow Doppler ultrasound, while material characteristics were adopted from the literature. A numerical finite element analysis (FEA) of the blood flow was performed by solving the governing equations for the viscous incompressible flow. The physical quantities calculated at the systolic and diastolic peak moment were velocity, pressure, shear stress and drag forces. RESULTS: We reported a case of a 50-year-old patient with a large RAVM and adjacent renal cyst, who unsuccessfully underwent two attempts of embolization that resulted in the consequent nephrectomy. FEA showed that the cyst had a very low pressure intensity and velocity field (with unstable flow in diastolic peak). For both systolic and diastolic moments, increased values of wall shear stress were found on the places with intensive wall calcification. Unusually high values of drag force which would likely explain the presence of pressure in the cystic formation were found on the infero-medial side where the cyst wall was the thinnest and where the flow streamlines converged. CONCLUSIONS: FEA showed that the hemodynamics of the cyst-RAVM complex was unstable making it prone to rupture. Clinically established diagnosis of imminent rupture together with unfavorable hemodynamics of the lesion consequently made additional attempts of embolization risky and unsuccessful leading to total nephrectomy.

Occlusal load distribution through the cortical and trabecular bone of the human mid-facial skeleton in natural dentition: a three-dimensional finite element study.

Understanding of the occlusal load distribution through the mid-facial skeleton in natural dentition is essential because alterations in magnitude and/or direction of occlusal forces may cause remarkable changes in cortical and trabecular bone structure. Previous analyses by strain gauge technique, photoelastic and, more recently, finite element (FE) methods provided no direct evidence for occlusal load distribution through the cortical and trabecular bone compartments individually. Therefore, we developed an improved three-dimensional FE model of the human skull in order to clarify the distribution of occlusal forces through the cortical and trabecular bone during habitual masticatory activities. Particular focus was placed on the load transfer through the anterior and posterior maxilla. The results were presented in von Mises stress (VMS) and the maximum principal stress, and compared to the reported FE and strain gauge data. Our qualitative stress analysis indicates that occlusal forces distribute through the mid-facial skeleton along five vertical and two horizontal buttresses. We demonstrated that cortical bone has a priority in the transfer of occlusal load in the anterior maxilla, whereas both cortical and trabecular bone in the posterior maxilla are equally involved in performing this task. Observed site dependence of the occlusal load distribution may help clinicians in creating strategies for implantology and orthodontic treatments. Additionally, the magnitude of VMS in our model was significantly lower in comparison to previous FE models composed only of cortical bone. This finding suggests that both cortical and trabecular bone should be modeled whenever stress will be quantitatively analyzed.

Computer methods for follow-up study of hemodynamic and disease progression in the stented coronary artery by fusing IVUS and X-ray angiography.

Despite a lot of progress in the fields of medical imaging and modeling, problem of estimating the risk of in-stent restenosis and monitoring the progress of the therapy following stenting still remains. The principal aim of this paper was to propose architecture and implementation details of state of the art of computer methods for a follow-up study of disease progression in coronary arteries stented with bare-metal stents. The 3D reconstruction of coronary arteries was performed by fusing X-ray angiography and intravascular ultrasound (IVUS) as the most dominant modalities in interventional cardiology. The finite element simulation of plaque progression was performed by coupling the flow equations with the reaction-diffusion equation applying realistic boundary conditions at the wall. The alignment of baseline and follow-up data was performed automatically by temporal alignment of IVUS electrocardiogram-gated frames. The assessment was performed using three six-month follow-ups of right coronary artery. Simulation results were compared with the ground truth data measured by clinicians. In all three data sets, simulation results indicated the right places as critical. With the obtained difference of 5.89 ± ~4.5% between the clinical measurements and the results of computer simulations, we showed that presented framework is suitable for tracking the progress of coronary disease, especially for comparing face-to-face results and data of the same artery from distinct time periods.