Deep learning models for classifying normal fetal cardiac ultrasound views
10.13929/j.issn.1003-3289.2025.01.015
- VernacularTitle:应用深度学习模型分类正常胎儿心脏超声切面
- Author:
Shuhao SONG
1
;
Shi ZENG
Author Information
1. 中南大学湘雅二医院超声诊断科,湖南长沙 410011
- Publication Type:Journal Article
- Keywords:
fetal heart;
echocardiography;
deep learning
- From:
Chinese Journal of Medical Imaging Technology
2025;41(1):70-73
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of four deep learning(DL)models for classifying 7 cardiac ultrasound views of normal fetus.Methods Two hundred normal fetuses who received fetal cardiac ultrasound examinations in 18 to 24+6 weeks of gestation were retrospectively included and divided into training set(n=140)and test set(n=60)at a ratio of 7:3.Two-dimensional ultrasound images were collected,including three-vessel and trachea(3VT)view,apical four-chamber(A4C)view,aortic arch long-axis view,bicaval view,left ventricular outflow tract(LVOT)view,three-vessel(3V)view and right ventricular outflow tract(RVOT)view.After image preprocessing,image features were extracted,and then 4 different DL models were constructed for classifying normal fetal cardiac ultrasound views,i.e.Vision Transformer(ViT),Data-efficient Image Transformer(DeiT),Vision-long short term memory(ViL)and Multi-axis Vision Transformer(MaxViT).The classification performance of each model in test set was assessed with accuracy,precision,recall and F1 score.Gradient-weighted class activation mapping(Grad-CAM)was used to obtain heatmaps for visualizing regions with the most distinctive features on ultrasound images.Results All ViT,DeiT,ViL and MaxViT had excellent performance in classifying normal fetal cardiac ultrasound views in test set,among which MaxViT was the optimal one,with accuracy,precision,recall and F1 score of 98.93％,98.93％,98.95％and 98.93％,respectively.Grad-CAM visualization results indicated that for classification of 7 cardiac ultrasound views of normal fetus using DL models,the heart and vessels present as the deepest red color,indicating the greatest contribution to the classification,also got the highest attention these models.Conclusion The obtained 4 DL models,especially MaxViT,had good capability for classifying normal fetal cardiac ultrasound views,with the interpretability of classifying results validated by Grad-CAM.
