Open Access

ARTICLE

Investigation into the Intelligent Recognition of Common Congenital Heart Disease Based on Echocardiography

Yanan Pan^1,2, Weize Xu^2,3,4,*, Zhiwei Zhang⁴, Mengmeng Su⁴, Xiaohua Pan⁴, Jingjing Ye^1,2,4, Jin Yu^1,2,4,*
1 Department of Ultrasound Diagnosis, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
2 Zhejiang Key Laboratory of Neonatal Diseases, Hangzhou, China
3 Department of Cardiac Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
4 Binjiang Institute of Zhejiang University, Hangzhou, China
* Corresponding Author: Weize Xu. Email: ; Jin Yu. Email:

Structural and Congenital Heart Disease https://doi.org/10.32604/schd.2026.079240

Received 17 January 2026; Accepted 23 June 2026; Published online 03 July 2026

Abstract

Background: Congenital heart disease (CHD) is characterized by a high incidence and low detection rate. The shortage of pediatric echocardiologists limits the routine ultrasound screening for CHD in many institutions. Deep learning has emerged as a promising tool to enhance the CHD diagnosis. This study aims to develop and evaluate a two-branch, two-stage intelligent algorithm (OBICnet) based on echocardiographic images to predict and classify common CHD in pediatric patients. Methods: A total of 8543 static color Doppler images from 3134 children were analyzed, with each child contributing 1 to 7 standard sectional views. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The external validation dataset included 350 color Doppler images of 50 children. The OBICnet architecture comprises two distinct branches and functions through a two-stage processing pipeline. Branch 1: The target detection network (YOLOv5) identified and extracted lesion regions, followed by disease classification using the image classification network (ResNet50). Branch 2: The ResNet50 network directly classified diseases by extracting features from whole image. Joint learning mixed scheduled sampling was applied during training to optimize the OBICnet model, with final disease classification achieved by integrating results from both branches. The OBICnet model’s performance was evaluated by comparing it with other algorithms and through external validation. Results: The OBICnet model categorized normal cases and three common CHD conditions: atrial septal defect + patent foramina ovale (ASD + PFO), ventricular septal defect (VSD), and patent ductus arteriosus (PDA). The accuracy of the single-image disease classification algorithm was 98.32%, with the AUC value of 0.99. For the multi-view disease classification, the accuracy reached 98.97%. External validation demonstrated that the accuracy of the single-image disease classification algorithm based on the OBICnet model was 92.30%, while the multi-view disease classification accuracy was 90.50%. Conclusions: The OBICnet model exhibited remarkable recognition capabilities for ultrasound images, accurately classifying common CHD in children. This model holds clinical potential for advancing teaching, standardizing CHD ultrasound screening and promoting large-scale echocardiographic screening of CHD in the future.

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Keywords

Congenital heart disease; image classification; multi-view analysis; object detection

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