Automatic Detection of Valvular Regurgitation by Echocardiography Based on Deep Learning
10.3969/j.issn.1005-5185.2025.02.007
- VernacularTitle:基于深度学习的超声心动图自动识别瓣膜反流
- Author:
Mate GUO
1
;
Yanjie SONG
;
Chan SHI
;
Shimin SUN
;
Jia MA
;
Bohan LIU
;
Qiushuang WANG
;
Liwei ZHANG
;
Feifei YANG
Author Information
1. 解放军总医院第四医学中心心内科,北京 100037
- Publication Type:Journal Article
- Keywords:
Deep learning;
Echocardiography;
Heart valve diseases;
Mitral valve insufficiency;
Aortic valve insufficiency;
Diagnosis
- From:
Chinese Journal of Medical Imaging
2025;33(2):147-151
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To investigate the feasibility of a deep learning framework to automatically analyze echocardiographic color Doppler videos in detecting valvular regurgitation.Materials and Methods This study retrospectively collected echocardiographic images of 1 109 patients with valvular regurgitation in the Fourth Medical Center of PLA General Hospital,from June 2015 to September 2019 as the training and validation sets.A prospective continuous collection of 1 562 echocardiography images was used as the test set in the Fourth Medical Center of PLA General Hospital from May 13 to June 13,2023,including 378 cases of mitral regurgitation and 223 cases of aortic regurgitation.This study developed deep learning networks to establish view classification model and valvular regurgitation recognition model,including the efficiency of section classification of deep learning models.Results The deep learning view classification model in this study could automatically identify two views for diagnosing mitral regurgitation and aortic regurgitation.The recognition accuracy for the parasternal long axis color Doppler view and the apical four chamber mitral color Doppler view was 1.00 and 0.93,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model for diagnosing mitral regurgitation were 0.847,0.852,0.849 and 0.930,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model in diagnosing aortic regurgitation were 0.857,0.861,0.859 and 0.940,respectively.Conclusion Deep learning algorithms can automatically identify valvular regurgitation and have the potential to become a screening tool for valvular heart disease.
