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Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a
Na Xu1,2, Tong Han3,4, Bingxuan Huang1,2
1Department of Medical Ultrasonics, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, No. 7019 Yitian Road, Shenzhen, China.
Background:
Hip ultrasound is the first-line tool to identify developmental dysplasia of the hip (DDH) among suspected infants, yet it has limitations including poor reproducibility and high diagnostic error rates. This study aims to develop and validate a deep convolutional neural network algorithm, named HipSonoNeuNet model (HSNN), using multicenter hip ultrasound data.
Methods:
This multicenter cross-sectional study combined data from 22 Chinese hospitals (September 2022-January 2025), enrolling 3082 participants. A total of 7286 hip ultrasound images (1429 dynamic, 5857 static) were collected and were divided into three datasets. The study was conducted in three phases. Phase I trained the models using 2431 participants (Dataset 1). Phase II compared diagnostic performance between radiologists of varied experience and the model across 500 participants (Dataset 2). Phase III prospectively validated the model's generalizability with 151 participants (Dataset 3).
Findings:
In Phase I, the HSNN yielded AUC of 0.99 (95% CI: 0.99-1.00), sensitivity of 1.00 (95% CI: 0.99-1.00), specificity of 0.91 (95% CI: 0.88-1.00), F1 score of 0.90 (95% CI: 0.87-1.00) on internal test dataset. In Phase II, the HSNN achieved an accuracy of 0.94 (95% CI: 0.88-1.00), AUC of 0.99 (95% CI: 0.99-1.00), sensitivity of 1.00 (95% CI: 0.99-1.00), specificity of 0.94 (95% CI: 0.87-1.00), F1 score of 0.58 (95% CI: 0.50-0.66), and strong agreement with expert (κ = 0.77). AI assistance improved all 7 junior radiologists' diagnostic performance (accuracy from 0.90 to 0.93, AUC from 0.80 to 0.95, sensitivity from 0.69 to 0.97) and reduced examination time with enhanced interobserver agreement. In Phase III, the model maintained robust performance (accuracy = 0.92, AUC = 0.99, sensitivity = 1.00, κ with experts = 0.76).
Interpretation:
The HSNN demonstrates accurate, robust, and generalizable performance in DDH detection.
Funding:
1. Guangdong High-level Hospital Construction Fund (SZGSP012), 2. Shenzhen Clinical Research Center (20220819113341005) "Shenzhen Clinical Research Center for Child Health and Disease (szcrc2024_005)", 3. Guangdong Medical Research Funded Project (A2024019), 4. Shenzhen Science and Technology Innovation Commission General Program for Basic Research (JCYJ20220530160000001), and National Natural Science Foundation of China (82271996).
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