To explore the value of artificial intelligence in ultrasound screening of congenital heart disease in children.

A total of 8543 static color Doppler ultrasound images of normal and abnormal cardiac structures from the Children's Hospital Affiliated to Zhejiang University School of Medicine from September 2021 to February 2022 were selected and divided into a training set of 6871 images, a validation set of 833 images, and a test set of 839 images at a ratio of 8:1:1. The OBICnet model was constructed, and its recognition performance was evaluated using F1 score, accuracy, precision, recall, specificity, misdiagnosis rate, and missed diagnosis rate. The performance of the OBICnet model was compared with that of the ResNet50 and GBCnet models. Additionally, 350 static color Doppler images of 50 children with abnormal cardiac structures who visited the Children's Hospital Affiliated to Zhejiang University School of Medicine from November 2022 to January 2023 were collected as external validation data. Twenty-one grassroots ultrasound physicians were divided into three groups based on their years of ultrasound experience: junior, intermediate, and senior. The recognition performance was compared between the OBICnet model and the three groups of physicians on the external validation set.

Compared with ResNet50 and GBCnet models, the OBICnet model had the best recognition performance, with the highest F1 score, accuracy, and precision, which were 97.0%, 98.3%, and 96.7%, respectively, and the lowest misdiagnosis rate and missed diagnosis rate, which were 1.6% and 3.0%, respectively. In the external validation set, the OBICnet model's recognition accuracy, precision, and specificity for normal and abnormal images were 94.6%, 91.4%, and 97.6%, respectively. The recognition accuracy of senior, intermediate, and junior physicians was 89.4%, 83.3%, and 67.8%, respectively, the precision was 78.9%, 73.5%, and 39.3%, respectively, and the specificity was 91.5%, 91.6%, and 81.4%, respectively. The differences in recognition performance between the OBICnet model and the physician groups were statistically significant (all adjusted P<0.017). The recognition sensitivity of the OBICnet model for normal and abnormal images was higher than that of the intermediate and junior physician groups (88.5% vs 61.5% and 31.8%, adjusted P<0.017). The missed diagnosis rate and misdiagnosis rate of the OBICnet model were 11.5% and 2.4%, respectively, which were significantly lower than those of the three groups of physicians (all adjusted P<0.017).

The OBICnet model exhibits superior recognition performance for normal and abnormal cardiac ultrasound images, and it has appreciated application value in the ultrasound screening of congenital heart disease in children.