Mitral Valve

The mitral valve, a critical heart valve, is the subject of intense research focused on improving its diagnosis and treatment. Current efforts leverage machine learning and deep learning, employing architectures like U-Nets, DenseNets, and convolutional neural networks, to automate the analysis of echocardiograms and CT scans for accurate mitral valve segmentation, measurement, and the detection of mitral regurgitation. These advancements aim to reduce diagnostic errors, improve treatment planning (including transcatheter edge-to-edge repair), and ultimately enhance patient outcomes by enabling faster, more reliable assessments of mitral valve health.

Papers

July 31, 2024

Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification
Zhe Liu, Xiliang Zhu, Tong Han, Yuhao Huang, Jian Wang, Lian Liu, Fang Wang, Dong Ni, Zhongshan Gou, Xin Yang
Distribution Detection Ultrasound Dataset Residual Information Unsupervised Out of Distribution Detection Residual Diffusion Unsupervised Out of Distribution Mitral Valve

January 24, 2024

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
Deep Learning Machine Learning Mitral Valve

October 7, 2023

Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging
Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau
Machine Learning Novel Semi Supervised Mitral Valve 3D Cardiac MRI

May 1, 2023

CNN-based fully automatic mitral valve extraction using CT images and existence probability maps
Yukiteru Masuda, Ryo Ishikawa, Toru Tanaka, Gakuto Aoyama, Keitaro Kawashima, James V. Chapman, Masahiko Asami, Michael Huy Cuong Pham, Klaus Fuglsang Kofoed, Takuya Sakaguchi, Kiyohide Satoh
CT Image Probability MAP Mitral Valve

February 21, 2023

A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography
Riccardo Munafò, Simone Saitta, Giacomo Ingallina, Paolo Denti, Francesco Maisano, Eustachio Agricola, Alberto Redaelli, Emiliano Votta
Transesophageal Echocardiography Tubular Structure Mitral Valve

January 20, 2023

Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation
Christian Schmidt, Heinrich Martin Overhoff
Neural Network Medical Image Segmentation Estimation Task Deep Learning Based Echocardiography Segmentation Cardiac Image Segmentation Mitral Valve

November 4, 2022

Automatic classification of deformable shapes
Hossein Dabirian, Radmir Sultamuratov, James Herring, Carlos El Tallawi, William Zoghbi, Andreas Mang, Robert Azencott
Automatic Classification Diffeomorphic Image Registration 3D Surface Deformable Shape Diffeomorphic Transformation Mitral Valve

Mitral Valve

Papers

Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification

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

Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging

CNN-based fully automatic mitral valve extraction using CT images and existence probability maps

A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography

Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation

Automatic classification of deformable shapes