Predicting Mortality after Transcatheter Aortic Valve Replacement using Preprocedural CT [Scientific Reports 2024]
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Updated
May 5, 2026 - Python
Predicting Mortality after Transcatheter Aortic Valve Replacement using Preprocedural CT [Scientific Reports 2024]
This project aims to improve endovascular procedures like TAVI using robotic assistance. It focuses on real-time path-planning algorithms and ultrasonic and electromagnetic sensors to enhance catheter navigation, aiming to increase surgical success rates and minimise long-term side effects.
Work with XBRL filings above Arelle: one parse, portable models
Research tool for continuous NCC-to-LV-myocardium membranous-septum candidate segmentation on cardiac CTA
ISSI BARBASH WEBSITE
Interpretable machine learning framework for predicting mid-term mortality and major periprocedural complications after transcatheter aortic valve implantation (TAVI) using pre-procedural clinical, echocardiographic, CT, and cusp-specific aortic valve calcium topography data. Includes reproducible survival and classification pipelines following TRI
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