Abstract
Background: Developmental dysplasia of the hip (DDH) is a prevalent issue in infants, with ultrasound crucial for early detection. Existing automatic diagnostic models lack precision due to noise, but 3D technology may enhance it. This study aimed to create and assess a deep-learning-based model for automated DDH diagnosis by using 3D transformation technology on two-dimensional ultrasound images. Methods: A retrospective study of 417 infants at risk of DDH used ultrasound images, combining convolutional neural networks and image processing. The images were analyzed using algorithms such as HigherHRNet-W48. The approach included apex point estimation, signal heterogeneity analysis of ilium, which focused on the bony area with high intensity and evaluate ilium rotation, alpha angle creation, and the establishment of a comprehensive method for DDH diagnosis. Results: Key findings include: (1) Superior accuracy in apex point estimation by the HigherHRNet-W48 model, even better than orthopedic residents. (2) Thorough quality assessments of ultrasound images, leading to qualified and disqualified categories, with qualified images displaying notably lower error rates. (3) The AUC of the model for DDH detection in the qualifying images was 0.92, exceeding the diagnostic accuracy of the resident, indicating the diagnostic capability of the tool. Conclusions: The study developed a deep-learning-based model for DDH detection in infants, melding 3D technology with deep learning to address challenges like noise and rotation in ultrasound images. The study's innovation demonstrated a comparative accuracy to specialized evaluations, even with non-specialist images, highlighting its potential to assist novice sonographers and enhance diagnostic precision.