Use of deep learning techniques for early detection of hip dysplasia in infants through pelvic radiographs

Abstract

Accurate and early classification of Developmental Hip Dysplasia (DHD) in pediatric patients is imperative for effective treatment, thereby reducing long-term complications. Conventional manual diagnosis methodologies show limitations, mainly due to interobserver variability and the need for specialized expertise, leading to inconsistent or delayed diagnoses. This article addresses this issue by implementing advanced deep learning techniques to analyze pelvic radiographs of 3- to 6-month-old infants, using ResNet50 to overcome the challenges of gradient fading and provide accurate assessments. Additionally, we address issues of privacy, sparsity, and imbalance of medical data by applying Generative Adversarial Networks (GANs) to generate high-quality synthetic data, thus enriching our training set and strengthening the robustness of our model. The integration of the proposed system with ResNet50 and GANs techniques was key to achieving an accuracy of 97.3% for the classification of pelvic radiographs with DHD.