Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning
  and Deep Learning Techniques
Tejas Vyas Mohsena Chowdhury Xiaojiao Xiao Mathias Claeys Géraldine Ong Guanghui Wang
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.