Mitral regurgitation detection and central/eccentric classification using transformer-based deep learning in
Xiaofang Zhong1, Jiancheng Zhang2,3,4, Yuanyuan Sheng1
1Department of Ultrasound, Shenzhen People's Hospital, (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, China.
Journal of Applied Clinical Medical Physics
|April 20, 2026
Summary
A new deep learning model can automatically detect mitral regurgitation (MR) in echocardiograms with high accuracy. This AI tool also classifies MR types and improves diagnostic efficiency.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Mitral regurgitation (MR) diagnosis can be enhanced by automated echocardiography for earlier detection.
- A deep learning (DL) framework was developed to automatically detect MR in echocardiography videos.
- The framework classifies regurgitation into central or eccentric types.
Purpose of the Study:
- To develop and validate a Transformer-based deep learning model for automated MR detection in echocardiography.
- To assess the model's ability to differentiate between central and eccentric MR.
- To evaluate the model's diagnostic performance across multiple echocardiographic views.
Main Methods:
- A Transformer-based deep learning model was designed for automated MR detection in Doppler videos.
- The model was trained, validated, and tested on retrospective datasets.
- An independent prospective dataset of 217 patients was used for validation.
Main Results:
- The model achieved high diagnostic accuracy for MR detection (0.94 retrospective, 0.92 prospective), comparable to physicians.
- It accurately classified central and eccentric MR (0.93 accuracy).
- Multi-view analysis improved diagnostic performance over single-view analysis.
Conclusions:
- The Transformer-based algorithm automates MR detection and classification, enhancing clinical workflow efficiency.
- The model can screen multi-view echocardiograms for MR presence and characteristics.
- This AI tool assists clinicians in diagnosing and classifying mitral regurgitation.
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