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Mitral Transcatheter Edge-to-Edge Repair (mTEER) is a medical procedureutilized for the treatment of mitral valve disorders. However, predicting theoutcome of the procedure poses a significant challenge. This paper makes thefirst attempt to harness classical machine learning (ML) and deep learning (DL)techniques for predicting mitral valve mTEER surgery outcomes. To achieve this,we compiled a dataset from 467 patients, encompassing labeled echocardiogramvideos and patient reports containing Transesophageal Echocardiography (TEE)measurements detailing Mitral Valve Repair (MVR) treatment outcomes. Leveragingthis dataset, we conducted a benchmark evaluation of six ML algorithms and twoDL models. The results underscore the potential of ML and DL in predictingmTEER surgery outcomes, providing insight for future investigation andadvancements in this domain.

Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques