Cardiac Resynchronization Therapy

Cardiac resynchronization therapy (CRT) aims to improve heart function in patients with electrical dyssynchrony, but predicting which patients will benefit remains challenging. Current research focuses on developing improved predictive models using machine learning, particularly deep learning architectures like convolutional neural networks, often incorporating data from electrocardiograms (ECGs), cardiac magnetic resonance imaging (CMR), and single-photon emission computed tomography (SPECT) myocardial perfusion imaging. These models leverage techniques like transfer learning and uncertainty quantification to enhance accuracy and efficiency, potentially reducing the need for extensive imaging and improving patient selection for CRT. Ultimately, advancements in predictive modeling promise to optimize CRT treatment, leading to better patient outcomes and more effective resource allocation.

Papers

September 15, 2023

A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Kristoffer Larsen, Chen Zhao, Joyce Keyak, Qiuying Sha, Diana Paez, Xinwei Zhang, Guang-Uei Hung, Jiangang Zou, Amalia Peix, Weihua Zhou
New Machine Uncertainty Quantification Multi Stage Ejection Fraction Myocardial Perfusion Cardiac Resynchronization Therapy

August 29, 2023

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Tareen Dawood, Chen Chen, Baldeep S. Sidhua, Bram Ruijsink, Justin Goulda, Bradley Porter, Mark K. Elliott, Vishal Mehta, Christopher A. Rinaldi, Esther Puyol-Anton, Reza Razavi, Andrew P. King
Deep Learning Classification Code Cardiac Magnetic Resonance Overconfident Prediction Uncertainty Aware Training Cardiac Resynchronization Therapy

June 2, 2023

A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Zhuo He, Hongjin Si, Xinwei Zhang, Qing-Hui Chen, Jiangang Zou, Weihua Zhou
New Method Non Negative Textual Response Single Lead Electrocardiogram Data Deep Transfer Cardiac Resynchronization Therapy

May 4, 2023

A new method using deep learning to predict the response to cardiac resynchronization therapy
Kristoffer Larsena, Zhuo He, Chen Zhao, Xinwei Zhang, Quiying Sha, Claudio T Mesquitad, Diana Paeze, Ernest V. Garciaf, Jiangang Zou, Amalia Peix, Weihua Zhou
Deep Learning New Method Non Negative Textual Response Myocardial Perfusion Cardiac Resynchronization Therapy

December 7, 2022

Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms
Alireza Sadeghi, Alireza Rezaee, Farshid Hajati
Electrocardiogram Data Cardiac Resynchronization Therapy

November 11, 2022

Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI
Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Frederick H. Epstein, Amit R. Patel, Miaomiao Zhang
Multitask Learning Human Heart Atrial Scar Cardiac Resynchronization Therapy

Cardiac Resynchronization Therapy

Papers

A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

A new method using deep learning to predict the response to cardiac resynchronization therapy

Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms

Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI