Abstract
AIM: Deep learning (DL), a branch of artificial intelligence (AI), has been applied to diagnose developmental dysplasia of the hip (DDH) on pelvic radiographs and ultrasound (US) images. This technology can potentially assist in early screening, enable timely intervention and improve cost-effectiveness. We conducted a systematic review to evaluate the diagnostic accuracy of the DL algorithm in detecting DDH. METHODS: PubMed, Medline, EMBASE, EMCARE, the clinicaltrials.gov (clinical trial registry), IEEE Xplore and Cochrane Library databases were searched in October 2024. Prospective and retrospective cohort studies that included children (< 16 years) at risk of or suspected to have DDH and reported hip ultrasonography (US) or X-ray images using AI were included. A review was conducted using the guidelines of the Cochrane Collaboration Diagnostic Test Accuracy Working Group. Risk of bias was assessed using the QUADAS-2 tool. RESULTS: Twenty-three studies met inclusion criteria, with 15 (n = 8315) evaluating DDH on US images and eight (n = 7091) on pelvic radiographs. The area under the curve of the included studies ranged from 0.80 to 0.99 for pelvic radiographs and 0.90-0.99 for US images. Sensitivity and specificity for detecting DDH on radiographs ranged from 92.86% to 100% and 95.65% to 99.82%, respectively. For US images, sensitivity ranged from 86.54% to 100% and specificity from 62.5% to 100%. CONCLUSION: AI demonstrated comparable effectiveness to physicians in detecting DDH. However, limited evaluation on external datasets restricts its generalisability. Further research incorporating diverse datasets and real-world applications is needed to assess its broader clinical impact on DDH diagnosis.