Machine Learning Models Predict the Need of Amputation and/or Peripheral Artery Revascularization in Hypertensive Patients Within 7-Years Follow-Up.

Lower extremity amputation and requirement of peripheral artery revascularization are common outcomes of undiagnosed peripheral artery disease patients. In the current work, prediction models for the need of amputation or peripheral revascularization focused on hypertensive patients within seven years follow up are employed. We applied machine learning (ML) models using classifiers such as Extreme Gradient Boost (XGBoost), Random Forest (RF) and Adaptive Boost (AdaBoost), that will allow clinicians to identify the patients at risk of these two endpoints using simple clinical data. We used the non-interventional cohort of the getABI study in the primary care setting, selecting 4,191 hypertensive patients out of 6,474 patients with age over 65 years old and followed up for vascular events or death up to 7 years. During this follow up period, 150 patients underwent either amputation or peripheral revascularization or both. Accuracy, Specificity, Sensitivity and Area under the receiver operating characteristic curve (AUC) were estimated for each machine learning model. The results demonstrate Random Forest as the most accurate model for the prediction of the composite endpoint in hypertensive patients within 7 years follow-up, achieving 73.27 % accuracy.Clinical Relevance-This study assists clinicians to better predict and treat these serious outcomes, amputation and peripheral revascularization in hypertensive patients.

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.

Machine Learning Models for Cardiovascular Disease Events Prediction.

Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical and machine learning (ML) models, In this work, we utilize clinical CVD risk factors and biochemical data using machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) and Adaptive Boosting (AdaBoost) to predict death caused by CVD within ten years of follow-up. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and 2943 patients were included in the analysis (484 annotated as dead due to CVD). We calculated the Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. The findings of the comparative analysis show that Logistic Regression has been proven to be the most reliable algorithm having accuracy 72.20 %. These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.

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.

Gait and balance patterns related to Free-Walking and TUG tests in Parkinson's Disease based on plantar pressure data.

Continuous monitoring of patients with Parkinson's Disease (PD) is critical for their effective management, as early detection of improvement or degradation signs play an important role on pharmaceutical and/or interventional plans. Within this work, a group of seven PD patients and a group of ten controls performed a set of exercises related to the evaluation of PD gait. Plantar pressure signals were collected and used to derive a set of analytics. Statistical tests and feature selection approaches revealed that the spatial distribution of the Center of Pressure during a static balance exercise is the most discriminative analytic and may be used for every-day monitoring of the patients. Results have revealed that out of the 28 features extracted from the collected signals, 10 were statistically significant (p < 0.05) and can be used to machine learning algorithms and/or similar approaches.

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.

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.

A hybrid plaque characterization method using intravascular ultrasound images.

BACKGROUND: Intravascular ultrasound (IVUS) is an invasive imaging modality that provides high resolution cross-sectional images permitting detailed evaluation of the lumen, outer vessel wall and plaque morphology and evaluation of its composition. Over the last years several methodologies have been proposed which allow automated processing of the IVUS data and reliable segmentation of the regions of interest or characterization of the type of the plaque. OBJECTIVE: In this paper we present a novel methodology for the automated identification of different plaque components in grayscale IVUS images. METHODS: The proposed method is based on a hybrid approach that incorporates both image processing techniques and classification algorithms and allows classification of the plaque into three different categories: Hard Calcified, Hard-Non Calcified and Soft plaque. Annotations by two experts on 8 IVUS examinations were used to train and test our method. RESULTS: The combination of an automatic thresholding technique and active contours coupled with a Random Forest classifier provided reliable results with an overall classification accuracy of 86.14%. CONCLUSIONS: The proposed method can accurately detect the plaque using grayscale IVUS images and can be used to assess plaque composition for both clinical and research purposes.