Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques
This work addresses a significant problem for medical professionals and patients by providing a first attempt at using ML/DL to predict mTEER surgery outcomes, though it appears incremental as it benchmarks existing methods on new data.
This paper tackled the challenge of predicting outcomes for Mitral Transcatheter Edge-to-Edge Repair (mTEER) surgery by applying machine learning and deep learning techniques to a dataset of 467 patients with echocardiogram videos and TEE measurements, achieving results that demonstrate the potential of these methods for future advancements.
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.