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

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

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

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