Comprehensive Summary
This perspective article reviews how artificial intelligence (AI) can integrate multimodal imaging parameters to enhance the diagnosis and management of developmental dysplasia of the hip (DDH). The authors discuss how deep learning models have been applied to radiographs, CT, MRI, and ultrasound to automate key diagnostic features such as acetabular index, femoral head coverage, and joint congruency. They emphasize the potential of AI-driven feature extraction to standardize diagnosis across modalities, reduce interobserver variability, and improve early detection in infants. The article also highlights challenges such as dataset quality, algorithm bias, and the need for interpretability in clinical AI tools.
Outcomes and Implications
AI-enabled imaging analysis can substantially improve diagnostic accuracy and consistency in DDH, facilitating earlier and more objective identification of hip dysplasia. Integrating parameters from multiple modalities—radiography, MRI, CT, and ultrasound—may enable comprehensive assessment pipelines, assist in surgical planning, and standardize pediatric orthopedic diagnostics globally. The authors call for multicenter collaborations and standardized datasets to translate these AI advancements into clinical workflows.