Artificial intelligence in the diagnostic imaging of developmental dysplasia of the hip: a systematic review

Purpose: With the increased challenges in diagnosing DDH using traditional ultrasound imaging methods, accurate diagnosis is essential. This study assesses the effectiveness of AI in the imaging-based diagnosis of DDH through a systematic review. Methods: This review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered in Prospero (registration ID: CRD42024563606). A comprehensive search was conducted across Ovid MEDLINE, PubMed, Embase, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews. Studies were screened using selection criteria, and quality was assessed using standardised tools. Thematic content analysis was also performed. Of the 32 studies identified, 19 were included, with 15 undergoing quantitative analysis. The main outcome measures were sensitivity, specificity, accuracy, AUROC, PPV, and NPV. Median, median absolute deviation, Bonett-Price 95% confidence intervals, maximum, minimum, and interquartile ranges were calculated and presented in a box-and-whisker diagram. Results: In the 19 included studies, the median sensitivity was 90.0% and specificity was 93.2% across 36,907 patients. Fifteen studies reported diagnostic accuracy, with a median of 92.6%. Accuracy rates ranged from 79.2 to 99%. The most common model architecture was mask R-CNN. Four studies (21%) were judged to have a high risk of bias using the QUADAS-2 tool. Conclusions: AI technologies hold significant potential for enhancing the diagnostic accuracy of DDH. However, existing variability and bias across studies highlight the need for further standardisation and validation.