A Multimodal Advanced Approach for the Stratification of Carotid Artery Disease The scope of this paper is to present the novel risk stratification framework for carotid artery disease which is under development in the TAXINOMISIS study. The study is implementing a multimodal strategy, integrating big data and advanced modeling approaches, in order to improve the stratification and management of patients with carotid artery disease, who are at risk for manifesting cerebrovascular events such as stroke. Advanced image processing tools for 3D reconstruction of the carotid artery bifurcation together with hybrid computational models of plaque growth, based on fluid dynamics and agent based modeling, are under development. Model predictions on plaque growth, rupture or erosion combined with big data from unique longitudinal cohorts and biobanks, including multiomics, will be utilized as inputs to machine learning and data mining algorithms in order to develop a new risk stratification platform able to identify patients at high risk for cerebrovascular events, in a precise and personalized manner. Successful completion of the TAXINOMISIS platform will lead to advances beyond the state of the art in risk stratification of carotid artery disease and rationally reduce unnecessary operations, refine medical treatment and open new directions for therapeutic interventions, with high socioeconomic impact.

Vessel lumen segmentation in internal carotid artery ultrasounds with deep convolutional neural networks Carotid ultrasound is a screening modality used by physicians to direct treatment in the prevention of ischemic stroke in highrisk patients. It is a time intensive process that requires highly trained technicians and physicians. Evaluation of a carotid ultrasound requires identification of the vessel wall, lumen, and plaque of the carotid artery. Automated machine learning methods for these tasks are highly limited. We propose and evaluate here single and multipath convolutional Uneural network for lumen identification from ultrasound images. We obtained deidentified images under IRB approval from 98 patients. We isolated just the internal carotid artery ultrasound images for these patients giving us a total of 302 images. We manually segmented the vessel lumen, which we use as ground truth to develop and validate our model. With a basic simple convolutional UNet we obtained a 10fold crossvalidation accuracy of 95%. We also evaluated a dualpath UNet where we modified the original image and used it as a synthetic modality but we found no improvement in accuracy. We found that the sample size made a considerable difference and thus expect our accuracy to rise as we add more training samples to the model. Our work here represents a first successful step towards the automated identification of the vessel lumen in carotid artery ultrasound images and is an important first step in creating a system that can independently evaluate carotid ultrasounds.