<p>Deep vein thrombosis (DVT) is a silent killer with millions of fatalities worldwide. The condition starts with a small clot in a person’s leg with minimal to no manifestations in the beginning. Often, symptoms go undiagnosed, misdiagnosed, or completely unnoticed leading to the clot moving up through the right side of the heart and subsequently blocking an artery leading to the lungs (pulmonary embolism or PE) which can result in sudden death or a host of other serious symptoms.  It has been suggested that stasis (stagnant zones over a period of time, dependent on other factors such as age, or underlying medical conditions, such as cancer or covid19) in these pockets may increase the risk of clots by various mechanisms and the geometry of the damaged valve plays important role in determining this.&nbsp;&nbsp;A proactive approach in which leading-edge technology to predict DVT vulnerability, track progression, and provide patient care checkpoints is of clear benefit.&nbsp;</p><p>We developed a novel mathematical framework to extract the venous valve leaflet morphology extracted from in-vitro images using machine learning assisted stereological analysis.&nbsp;This is necessary to obtain a sufficiently accurate representation of morphology that could ultimately be tailored to specific patients.&nbsp;The extracted valve will be used to model the flow through the venous systems under various valve configurations and identify red-flag stagnant zones by a detailed, physics-based computational study of the blood flow through the leaflet models.&nbsp;&nbsp;</p>

A Novel Mathematical Framework for the Venous Valve Leaflet Morphology Extracted From In-Vitro Images Using Machine Learning Assisted Stereological Analysis

Paper Proceedings: https://asmedigitalcollection.asme.org/FEDSM/FEDSM2021/volume/85284

ASME 2021 Fluids Engineering Division Summer Meeting (FEDSM2021)

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