Deep learning echocardiographic intelligent model for evaluation on left ventricular regional wall motion abnormality
10.13929/j.issn.1003-3289.2024.08.004
- VernacularTitle:深度学习超声智能模型评价左心室节段性室壁运动异常
- Author:
Yonghuai WANG
1
,
2
;
Tianxin DONG
;
Chunyan MA
Author Information
1. 中国医科大学附属第一医院心血管超声科,辽宁沈阳 110002
2. 辽宁省影像医学临床医学研究中心,辽宁沈阳 110002
- Keywords:
ventricular function,left;
systolic function;
echocardiography;
deep learning
- From:
Chinese Journal of Medical Imaging Technology
2024;40(8):1135-1139
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)％,specificity of(89.44±7.31)％and accuracy of(85.22±4.37)％.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.
