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

arXiv: https://arxiv.org/abs/2401.13197 · HTML: https://mlsanity.com/paper/2401.13197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training](https://mlsanity.com/paper/2112.15555/) (2021)
- [Edge-aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI](https://mlsanity.com/paper/2307.01798/) (2023)
- [A Discriminative Channel Diversification Network for Image Classification](https://mlsanity.com/paper/2112.05861/) (2021)
- [Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks](https://mlsanity.com/paper/2102.05582/) (2021)
- [Gender, Smoking History and Age Prediction from Laryngeal Images](https://mlsanity.com/paper/2305.16661/) (2023)

## Built on by (most cited first, 1 of 1)

- [Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models](https://mlsanity.com/paper/2402.01067/) (2024)

## Abstract (arXiv)

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.