Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Enhancing risk prediction capabilities in patients with carotid artery disease using a 2-level computational approach.

One of the main causes of death worldwide is carotid artery disease, which causes increasing arterial stenosis and may induce a stroke. To address this problem, the scientific community aims to improve our understanding of the underlying atherosclerotic mechanisms, as well as to make it possible to forecast the progression of atherosclerosis. Additionally, over the past several years, developments in the field of cardiovascular modeling have made it possible to create precise three-dimensional models of patient-specific main carotid arteries. The aforementioned 3D models are then implemented by computational models to forecast either the progression of atherosclerotic plaque or several flow-related metrics which are correlated to risk evaluation. A precise representation of both the blood flow and the fundamental atherosclerotic process within the arterial wall is made possible by computational models, therefore, allowing for the prediction of future lumen stenoses, plaque areas and risk prediction. This work presents an attempt to integrate the outcomes of a novel plaque growth model with advanced blood flow dynamics where the deformed luminal shape derived from the plaque growth model is compared to the actual patient-specific luminal model in terms of several hemodynamic metrics, to identify the prediction accuracy of the aforementioned model. Pressure drop ratios had a mean difference of <3%, whereas OSI-derived metrics were identical in 2/3 cases.Clinical Relevance-This establishes the accuracy of our plaque growth model in predicting the arterial geometry after the desired timeline.

TAXINOMISIS: A cloud - based platform for risk profiling and patient specific management of the carotid artery disease.

Carotid Artery Disease is a complex multi-disciplinary medical condition causing strokes and several other disfunctions to individuals. Within this work, a cloud - based platform is proposed for clinicians and medical doctors that provides a comprehensive risk assessment tool for carotid artery disease. It includes three modeling levels: baseline data-driven risk assessment, blood flow simulations and plaque progression modeling. The proposed models, which have been validated through a wide set of studies within the TAXINOMISIS project, are delivered to the end users through an easy-to-use cloud platform. The architecture and the deployment of this platform includes interfaces for handling the electronic patient record, the 3D arterial reconstruction, blood flow simulations and risk assessment reporting. TAXINOMISIS, compared with both similar software approaches and with the current clinical workflow, assists clinicians to treat patients more effectively and more accurately by providing innovative and validated tools.Clinical Relevance - Asymptomatic carotid artery disease is a prevalent condition that affects a significant portion of the population, leading to an increased risk of stroke and other cardiovascular events. Early detection and appropriate treatment of this condition can significantly reduce the risk of adverse outcomes and improve patient outcomes. The development of a software tool to assist clinicians in the assessment and management of asymptomatic patients with carotid artery disease is therefore of great clinical relevance. By providing a comprehensive and reliable assessment of the disease and its risk factors, this tool will enable clinicians to make informed decisions regarding patient management and treatment. The impact of this tool on patient outcomes and the reduction of healthcare costs will be of great importance to both patients and the healthcare system.

An integrated rehabilitation system for the upper limb spasticity assessment and treatment: the Rehabotics passive exoskeletal system.

Limb spasticity is caused by stroke, multiple sclerosis, traumatic brain injury and various central nervous system pathologies such as brain tumors resulting in joint stiffness, loss of hand function and severe pain. This paper presents with the Rehabotics integrated rehabilitation system aiming to provide highly individualized assessment and treatment of the function of the upper limbs for patients with spasticity after stroke, focusing on the developed passive exoskeletal system. The proposed system can: (i) measure various motor and kinematic parameters of the upper limb in order to evaluate the patient's condition and progress, as well as (ii) offer a specialized rehabilitation program (therapeutic exercises, retraining of functional movements and support of daily activities) through an interactive virtual environment. The outmost aim of this multidisciplinary research work is to create new tools for providing high-level treatment and support services to patients with spasticity after stroke.Clinical Relevance- This paper presents a new passive exoskeletal system aiming to provide enhanced treatment and assessment of patients with upper limb spasticity after stroke.

Accurate adventitia reconstruction; significant or not in atherosclerotic plaque growth simulationsƒ A comparative study.

A reform in the diagnosis and treatment process is urgently required as carotid artery disease remains a leading cause of death in the world. To this purpose, all computational techniques are now being applied to enhancing the most cutting-edge diagnosis techniques. Computational modeling of plaque generation and evolution is being refined over the past years to forecast the atherosclerotic progression and the corresponding risk in patient-specific carotid arteries. A prerequisite to their implementation is the reconstruction of the precise three-dimensional models of patient-specific main carotid arteries. Even with the most sophisticated algorithms, accurate reconstruction of the arterial vessel is frequently difficult. Furthermore, there are several works of plaque growth modeling that ignore the reconstruction of the artery's outer layer in favor of a virtual one. In this paper, we investigate the importance of an accurate adventitia layer in plaque growth modeling. This is done as a comparative study by implementing a novel plaque growth model in two reconstructed carotid arterial segments using either their realistic or virtual adventitia layer as input. The results indicate that accurate adventitia reconstruction is of minor importance regarding species distributions and plaque growth in carotid segments, which initially did not contain any plaque regions.Clinical Relevance- The findings of this comparative study emphasize the importance of precise adventitia geometry in plaque growth modeling. As a result, this work sets a higher standard for publishing new plaque growth models.

A Machine Learning Model for the prediction of the progression of carotid arterial stenoses.

Atherosclerotic carotid plaque development results in a steady narrowing of the artery lumen, which may eventually trigger catastrophic plaque rupture leading to thromboembolism and stroke. The primary cause of ischemic stroke in the EU is carotid artery disease, which increases the demand for tools for risk stratification and patient management in carotid artery disease. Additionally, advancements in cardiovascular modeling over the past few years have made it possible to build accurate three-dimensional models of patient-specific primary carotid arteries. Computational models then incorporate the aforementioned 3D models to estimate either the development of atherosclerotic plaque or a number of flow-related parameters that are linked to risk assessment. This work presents an attempt to provide a carotid artery stenosis prognostic model, utilizing non-imaging and imaging data, as well as simulated hemodynamic data. The overall methodology was trained and tested on a dataset of 41 cases with 23 carotid arteries with stable stenosis and 18 carotids with increasing stenosis degree. The highest accuracy of 71% was achieved using a neural network classifier. The novel aspect of our work is the definition of the problem that is solved, as well as the amount of simulated data that are used as input for the prognostic model.Clinical Relevance-A prognostic model for the prediction of the trajectory of carotid artery atherosclerosis is proposed, which can support physicians in critical treatment decisions.

Detection of Asymptomatic Carotid Artery Stenosis through Machine Learning.

Carotid artery disease, the pathological condition of carotid arteries, is considered as the most significant cause of cerebral events and stroke. Carotid artery disease is considered as an inflammatory process, which involves the deposition and accumulation of atherosclerotic plaque inside the carotid intima, resulting in the narrowing of the arteries. Carotid artery stenosis (CAS) is either symptomatic or asymptomatic and its presence and location is determined by different imaging modalities, such as the carotid duplex ultrasound, the computed tomography angiography, the magnetic resonance angiography (MRA) and the cerebral angiography. The aim of this study is to present a machine learning model for the diagnosis and identification of individuals of asymptomatic carotid artery stenosis, using as input typical health data. More specifically, the overall model is trained with typical demographics, clinical data, risk factors and medical treatment data and is able to classify the individuals into high risk (Class 1-CAS group) and low risk (Class 0-non CAS group) individuals. In the presented study, we implemented a statistical analysis to check the data quality and the distribution into the two classes. Different feature selection techniques, in combination with classification schemes were applied for the development of our machine learning model. The overall methodology has been trained and tested using 881 cases (443 subjects in low risk class and 438 in high risk class). The highest accuracy 0.82 and an area under curve 0.9 were achieved using the relief feature selection technique and the random forest classification scheme.

Prediction of the atherosclerotic plaque development in carotid arteries; the effect of T-cells.

The carotid artery disease is one of the leading causes of mortality worldwide, as it leads to the progressive arterial stenosis that may result to stroke. To address this issue, the scientific community is attempting not only to enrich our knowledge on the underlying atherosclerotic mechanisms, but also to enable the prediction of the atherosclerotic progression. This study investigates the role of T-cells in the atherosclerotic plaque growth process through the implementation of a computational model in realistic geometries of carotid arteries. T-cells mediate in the inflammatory process by secreting interferon-y that enhances the activation of macrophages. In this analysis, we used 5 realistic human carotid arterial segments as input to the model. In particular, magnetic resonance imaging data, as well as, clinical data were collected from the patients at two time points. Using the baseline data, plaque growth was predicted and correlated to the follow-up arterial geometries. The results exhibited a very good agreement between them, presenting a high coefficient of determination R2=0.64.

Computational modelling of Human Femur after Total Hip Arthroplasty.

This paper presents the workflow for creating a 3D finite element model of a cementless femur-implant when in single leg-stance, using state-of-the-art computer-aided design software and a finite element solver. The model consists of two geometries for the cortical and trabecular bone tissue of the femur bone, and two geometries for the stem and femoral head of a commercial implant. Each part is assumed to behave as linear isotropic material. Although relatively simplistic in its form, the presented 3D finite element model can capture the area of higher Von Misses stress concentration compared to other models in the literature.

A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques.

Carotid artery disease is an inflammatory condition involving the deposition and accumulation of lipid species and leucocytes from blood into the arterial wall, which causes the narrowing of the carotid arteries on either side of the neck. Different imaging modalities can by implemented to determine the presence and the location of carotid artery stenosis, such as carotid ultrasound, computed tomography angiography (CTA), magnetic resonance angiography (MRA), or cerebral angiography. However, except of the presence and the degree of stenosis of the carotid arteries, the vulnerability of the carotid atherosclerotic plaques constitutes a significant factor for the progression of the disease and the presence of disease symptoms. In this study, our aim is to develop and present a machine learning model for the identification of high risk plaques using non imaging based features and non-invasive imaging based features. Firstly, we implemented statistical analysis to identify the most statistical significant features according to the defined output, and subsequently, we implemented different feature selection techniques and classification schemes for the development of our machine learning model. The overall methodology has been trained and tested using 208 cases of 107 cases of low risk plaques and 101 cases of high risk plaques. The highest accuracy of 0.76 was achieved using the relief feature selection technique and the support vector machine classification scheme. The innovative aspect of the proposed machine learning model is both the different categories of the utilized input features and the definition of the problem to be solved.

Computational modeling of atherosclerotic plaque progression in carotid lesions with moderate degree of stenosis.

Carotid atherosclerotic plaque growth leads to the progressive luminal stenosis of the vessel, which may erode or rupture causing thromboembolism and cerebral infarction, manifested as stroke. Carotid atherosclerosis is considered the major cause of ischemic stroke in Europe and thus new imaging-based computational tools that can improve risk stratification and management of carotid artery disease patients are needed. In this work, we present a new computational approach for modeling atherosclerotic plaque progression in real patient-carotid lesions, with moderate to severe degree of stenosis (>50%). The model incorporates for the first time, the baseline 3D geometry of the plaque tissue components (e.g. Lipid Core) identified by MR imaging, in which the major biological processes of atherosclerosis are simulated in time. The simulated plaque tissue production results in the inward remodeling of the vessel wall promoting luminal stenosis which in turn predicts the region of the actual stenosis progression observed at the follow-up visit. The model aims to support clinical decision making, by identifying regions prone to plaque formation, predict carotid stenosis and plaque burden progression, and provide advice on the optimal time for patient follow-up screening.

Serum Biomarkers in Carotid Artery Disease.

Carotid artery disease is considered a major cause of strokes and there is a need for early disease detection and management. Although imaging techniques have been developed for the diagnosis of carotid artery disease and different imaging-based markers have been proposed for the characterization of atherosclerotic plaques, there is still need for a definition of high-risk plaques in asymptomatic patients who may benefit from surgical intervention. Measurement of circulating biomarkers is a promising method to assist in patient-specific disease management, but the lack of robust clinical evidence limits their use as a standard of care. The purpose of this review paper is to present circulating biomarkers related to carotid artery diagnosis and prognosis, which are mainly provided by statistical-based clinical studies. The result of our investigation showed that typical well-established inflammatory biomarkers and biomarkers related to patient lipid profiles are associated with carotid artery disease. In addition to this, more specialized types of biomarkers, such as endothelial and cell adhesion, matrix degrading, and metabolic biomarkers seem to be associated with different carotid artery disease outputs, assisting vascular specialists in selecting patients at high risk for stroke and in need of intervention.

A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging.

The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.

The TAXINOMISIS Project: A multidisciplinary approach for the development of a new risk stratification model for patients with asymptomatic carotid artery stenosis.

INTRODUCTION: Asymptomatic carotid artery stenosis (ACAS) may cause future stroke and therefore patients with ACAS require best medical treatment. Patients at high risk for stroke may opt for additional revascularization (either surgery or stenting) but the future stroke risk should outweigh the risk for peri/post-operative stroke/death. Current risk stratification for patients with ACAS is largely based on outdated randomized-controlled trials that lack the integration of improved medical therapies and risk factor control. Furthermore, recent circulating and imaging biomarkers for stroke have never been included in a risk stratification model. The TAXINOMISIS Project aims to develop a new risk stratification model for cerebrovascular complications in patients with ACAS and this will be tested through a prospective observational multicentre clinical trial performed in six major European vascular surgery centres. METHODS AND ANALYSIS: The risk stratification model will compromise clinical, circulating, plaque and imaging biomarkers. The prospective multicentre observational study will include 300 patients with 50%-99% ACAS. The primary endpoint is the three-year incidence of cerebrovascular complications. Biomarkers will be retrieved from plasma samples, brain MRI, carotid MRA and duplex ultrasound. The TAXINOMISIS Project will serve as a platform for the development of new computer tools that assess plaque progression based on radiology images and a lab-on-chip with genetic variants that could predict medication response in individual patients. CONCLUSION: Results from the TAXINOMISIS study could potentially improve future risk stratification in patients with ACAS to assist personalized evidence-based treatment decision-making.

A biomechanical study of the effect of weight loading conditions on the mechanical environment of the hip joint endoprosthesis.

BACKGROUND: Total hip arthroplasty is one of the most successful orthopedic surgical procedures aiming to eliminate pain related to several types of hip arthritis and restore mobility. Obesity has been associated with an increased risk of complications after a total hip arthroplasty such as poor wound healing, periprosthetic joint infection, instability, and aseptic loosening. METHODS: This paper presents an in-vitro study on composite femoral models to investigate the impact of different weight loading conditions on the mechanical environment of the hip joint endoprosthesis considering normal-weight and overweight individuals from 70 to 110 kg. The micro strains on the femur during single-leg stance of gait were measured on critical stress points based on the Gruen femoral zones. FINDINGS: The micro strains increase as the weight increases implying that the displacement in the hip joint endoprosthesis is higher for overweight subjects enhancing the risk of failure. The highest increase was measured in Gruen zone 1 by 5.60% indicating that the great trochanter is subjected to higher stress shielding with increasing the weight. Also, the statistically significant increase of the micro strain values with increasing the weight in Gruen Zones 3 (2.91%), 5 (1.56%), and 11 (1.75%) may enhance the risk for a periprosthetic fracture at the lower region of the prosthesis. INTERPRETATION: This is the first biomechanical study which quantifies the effect of increasing weight loading conditions on the mechanical environment of the hip joint endoprosthesis considering different positions of evaluation.

Numerical evaluation of the mechanical environment of a fractured long bone for osteoporotic and non-osteoporotic subjects.

Competent fracture healing monitoring requires an extensive knowledge of the evolution of the mechanical environment of the healing bone during daily-life activities such as walking. Fractures are caused due to a traumatic incidence, while low trauma or fragility fractures can also occur due to osteoporosis. It is expected that the mechanical behavior of healing bones differs among osteoporotic and non-osteoporotic subjects. This work presents finite element simulations of gait analysis considering a fractured long bone at the hematoma stage. The aim is to investigate the evolution of the mechanical environment of the femur for an osteoporotic and a non-osteoporotic subject. This is the first computational study providing quantitative information for the impact of osteoporosis on the mechanical environment of the femur. It was shown, that higher deformation and equivalent stress values are calculated for osteoporotic bones during a gait cycle, while the highest values were observed in the femoral head.

Evaluation of ultrasonic scattering for different cortical bone porosities and excitation frequencies: A numerical study.

Quantitative ultrasound is a promising and relative recent method for the assessment of bone. In this work, the interaction of ultrasound with the porosity of cortical bone is investigated for different frequencies. Emphasis is given on the study of complex scattering effects induced by the propagation of an ultrasonic wave in osseous tissues. Numerical models of cortical bone are established with a porosity of 0, 5 and 10% corresponding to healthy homogeneous bone, healthy inhomogeneous bone and normal ageing, respectively. Different excitation frequencies are applied in the range 0.2-1 MHz. The scattering amplitude and the acoustic pressure are calculated for multiple angles and receiving positions focusing on the backward direction. The results indicate that the application of higher frequencies can better distinguish changes in the energy distribution in the backward direction due to alterations of the cortical porosity.

Numerical evaluation of the backward propagating acoustic field in healing long bones.

The propagation of ultrasound in healing long bones induces complex scattering phenomena due to the interaction of an ultrasonic wave with the composite nature of callus and osseous tissues. This work presents numerical simulations of ultrasonic propagation in healing long bones using the boundary element method aiming to provide insight into the complex scattering mechanisms and better comprehend the state of bone regeneration. Numerical models of healing long bones are established based on scanning acoustic microscopy images from successive postoperative weeks considering the effect of the nonhomogeneous callus structure. More specifically, the scattering amplitude and the acoustic pressure variation are calculated in the backward direction to investigate their potential to serve as quantitative and qualitative indicators for the monitoring of the bone healing process. The role of the excitation frequency is also examined considering frequencies in the range 0.2-1 MHz. The results indicate that the scattering amplitude decreases at later stages of healing compared to earlier stages of healing. Also, the acoustic pressure could provide supplementary qualitative information on the interaction of the scattered energy with bone and callus.

Boundary element simulation of ultrasonic backscattering during the fracture healing process.

Competent fracture healing monitoring and treatment requires an extensive knowledge of bone biology and microstructure. The use of non-invasive and non-radiating means for the monitoring of the bone healing process has gained significant interest in recent years. Ultrasound is considered as a modality which can contribute to the assessment of bone status during the healing process, as well as, enhance the rate of the tissues' ossification. This work presents boundary element simulations of ultrasound propagation in healing long bones to investigate the monitoring potential of backscattering parameters. The interaction of a plane wave at 100 kHz with the bone and the callus is examined by calculating the acoustic pressure and scattering amplitude in the backward direction. Callus is considered as a two-dimensional, non-homogeneous medium consisted of multiple layers with evolving material properties. It was shown that the backscattering parameters could potentially reflect the fracture healing progress.

Computational Study of the Effect of Cortical Porosity on Ultrasound Wave Propagation in Healthy and Osteoporotic Long Bones.

Computational studies on the evaluation of bone status in cases of pathologies have gained significant interest in recent years. This work presents a parametric and systematic numerical study on ultrasound propagation in cortical bone models to investigate the effect of changes in cortical porosity and the occurrence of large basic multicellular units, simply called non-refilled resorption lacunae (RL), on the velocity of the first arriving signal (FAS). Two-dimensional geometries of cortical bone are established for various microstructural models mimicking normal and pathological tissue states. Emphasis is given on the detection of RL formation which may provoke the thinning of the cortical cortex and the increase of porosity at a later stage of the disease. The central excitation frequencies 0.5 and 1 MHz are examined. The proposed configuration consists of one point source and multiple successive receivers in order to calculate the FAS velocity in small propagation paths (local velocity) and derive a variation profile along the cortical surface. It was shown that: (a) the local FAS velocity can capture porosity changes including the occurrence of RL with different number, size and depth of formation; and (b) the excitation frequency 0.5 MHz is more sensitive for the assessment of cortical microstructure.