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.

翻译：二尖瓣经导管缘对缘修复术（mTEER）是一种用于治疗二尖瓣疾病的医疗程序。然而，预测该手术的结果是一项重大挑战。本文首次尝试利用经典机器学习（ML）和深度学习（DL）技术来预测二尖瓣mTEER手术的结果。为此，我们整理了来自467名患者的数据集，包含已标注的超声心动图视频以及含有经食管超声心动图（TEE）测量的患者报告，详细记录了二尖瓣修复（MVR）治疗的结果。利用该数据集，我们对六种ML算法和两种DL模型进行了基准评估。结果凸显了ML和DL在预测mTEER手术结果方面的潜力，为该领域的未来研究和进展提供了见解。