Artificial Intelligence-Assisted Radiographic Diagnosis of Developmental Dysplasia of the Hip in Children: A Retrospective Study
Abstract
Introduction: Developmental dysplasia of the hip (DDH) is the most common pediatric musculoskeletal disorder, and its radiographic diagnosis may be affected by technical and interpretive variability. Artificial intelligence (AI), particularly through automated machine learning (AutoML) platforms, offers tools to standardize image assessment and assist in diagnostic classification. Objectives: To develop and evaluate an AI model for distinguishing between normal pediatric pelvic radiographs and those showing DDH using the Landing Lens platform. Materials and Methods: A retrospective study included 328 pelvic radiographs from patients aged 1 month to 14 years. Four pediatric orthopedic surgeons with varying levels of experience independently labeled the images. The final diagnosis was established by a simple majority, with consensus reached in cases of disagreement. The radiographs were divided into training (70%), validation (20%), and test (10%) sets. Model performance was assessed using accuracy, recall, specificity, F1 score, and agreement coefficients. Results: The model correctly classified 320 images (accuracy, 97.6%; recall, 92.8%; specificity, 98%; F1 score, 86.6%). Interobserver agreement was almost perfect (Fleiss’ kappa, 0.86), and agreement between the model and specialists increased with clinical experience (Cohen’s kappa, 0.85 for the senior observer). Conclusions: These findings support the use of AI as an adjunctive tool to standardize the radiographic diagnosis of DDH and reduce interobserver variability. External validation and multicenter studies are required before routine clinical implementation.Downloads
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