Artificial Intelligence-assisted Ultrasound for Developmental Dysplasia of the Hip: Systematic Review and Meta-analysis Across Segmentation, Angle Measurement, and Diagnostic Tasks.

RATIONALE AND

OBJECTIVES

Artificial intelligence (AI) applications for developmental dysplasia of the hip (DDH) assessment have emerged across multiple analytical tasks, yet comprehensive evidence synthesis remains limited. This systematic review and meta-analysis evaluated AI-assisted hip ultrasonography performance across segmentation, angle measurement, and diagnostic classification tasks in infants.

MATERIALS AND

METHODS

Following PROSPERO registration (CRD420251133940), we searched Medline/PubMed, Web of Science, Scopus, and Cochrane Library databases. Studies reporting AI performance for DDH ultrasonographic evaluation were included. Quality assessment employed QUADAS-2 and CLAIM checklists. Random-effects meta-analyses synthesized Dice Similarity Coefficients (DSC) for segmentation, Intraclass Correlation Coefficients (ICC) for angle measurements, and diagnostic accuracy metrics. Heterogeneity was assessed using I² statistics and prediction intervals.

RESULTS

Thirty-four studies met inclusion criteria. Segmentation performance (7 studies, n=3011) yielded pooled DSC of 0.876 (95% CI: 0.827-0.924). Angle measurement reliability (4 studies, n=2012) demonstrated pooled ICC of 0.883 (95% CI: 0.763-0.945) for alpha angles and 0.864 (95% CI: 0.653-0.951) for beta angles. Diagnostic classification showed pooled AUC of 0.937 (95% CI: 0.885-0.966) across 5 studies (n=3035), with pooled sensitivity 0.888 (95% CI: 0.840-0.923) and specificity 0.893 (95% CI: 0.735-0.961) from 10 studies (n=5341). Substantial heterogeneity (I²>75%) was observed across all domains. CLAIM adherence averaged 67.9%, with critical gaps in external validation (20.6%).

CONCLUSION

AI-assisted hip ultrasonography demonstrates strong performance across multiple tasks, supporting potential clinical application. However, high heterogeneity, limited external validation, and reporting deficiencies necessitate standardization before clinical deployment.