AI-Driven Detection of Developmental Dysplasia of the Hip in Pediatric Patients

Developmental Dysplasia of the Hip (DDH) is a condition affecting newborns and infants, where early diagnosis is crucial to prevent long-term complications. Manual interpretation of radiographs, the current standard for diagnosing DDH, is often time-consuming and prone to human error. This study presents a Convolutional Neural Network (CNN)-based approach for the automatic detection of DDH using pediatric pelvic radiographs. The CNN model achieved an accuracy of 95.5%, with a precision and recall of 96% for DDH cases, outperforming human radiologists in diagnostic accuracy. The use of Gradient-Weighted Class Activation Mapping (GRAD-CAM) provided insight into the model's decision-making process, confirming that the CNN focused on relevant anatomical regions. This study demonstrates the potential of AI to improve the accuracy and efficiency of DDH diagnosis, supporting radiologists in clinical practice.