{'fraction_non_alphanumeric': 0.022321428571428572, 'fraction_numerical': 0.0012755102040816326, 'mean_word_length': 5.276, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Mathematical Framework for the Venous Valve Leaflet Morphology Extracted From In-Vitro Images Using Machine Learning Assisted Stereological Analysis


 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 the valve pockets may increase the risk of clots due to stasis in combination with other factors increases the risk of Deep Venous Thrombosis (DVT) formation, blood stasis may also result in a decrease in the anticoagulants factors that prevent clots from forming, and if the vein wall is damaged this further increases the risk of clot formation. We propose a proactive framework to predict DVT vulnerability, track progression and provide patient care checkpoints is of clear benefit. The framework is based on leading-edge cloud computing technologies and promises to offer user-friendly Software- & Platform-as-a-Service (SaaS/PaaS) solutions via novel machine learning (ML) algorithm and high fidelity blood flow modeling through the venous network under various valve configurations. In this work, we will present the progress made towards the leaflet morphology extraction from in-vitro images using ML assisted stereological analysis for obtaining a sufficiently accurate representation of morphology. Ultimately, the workflow can be tailored to specific patients. The extracted valve is used to identify red-flag stagnant zones by a detailed, physics-based computational study of the blood flow through the leaflet models.