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

TL;DR

This study explores the application of machine learning and deep learning techniques to predict the outcomes of mitral valve mTEER surgeries, using a dataset of echocardiogram videos and patient reports, marking a pioneering effort in this area.

Contribution

It is the first to apply classical ML and DL methods to predict mTEER surgery outcomes using echocardiogram data and reports.

Findings

ML and DL models show promise in outcome prediction

Benchmark evaluation of six ML algorithms and two DL models conducted

Results provide insights for future research in medical outcome prediction

Abstract

Mitral Transcatheter Edge-to-Edge Repair (mTEER) is a medical procedure utilized for the treatment of mitral valve disorders. However, predicting the outcome of the procedure poses a significant challenge. This paper makes the first 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 echocardiogram videos and patient reports containing Transesophageal Echocardiography (TEE) measurements detailing Mitral Valve Repair (MVR) treatment outcomes. Leveraging this dataset, we conducted a benchmark evaluation of six ML algorithms and two DL models. The results underscore the potential of ML and DL in predicting mTEER surgery outcomes, providing insight for future investigation and advancements in this domain.