AI models demonstrate high diagnostic accuracy for identifying osteonecrosis of the femoral head in imaging dataArtificial intelligence shows high accuracy in detecting hip bone death

BACKGROUND: Osteonecrosis of the femoral head (ONFH) is a common cause of hip disability in clinical practice. Early and accurate diagnosis can delay or even halt disease progression. In recent years, AI models based on medical imaging have been increasingly applied to the diagnosis of ONFH; however, a systematic evaluation of their diagnostic accuracy remains lacking. OBJECTIVE: This study aims to synthesize the overall diagnostic accuracy of medical imaging-based AI models for ONFH and to inform clinical decision-making. METHODS: This systematic review was conducted in accordance with the PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies) guidelines and was prospectively registered in PROSPERO (CRD420261307216). We searched PubMed, Embase, Cochrane Library, and Web of Science up to March 8, 2026. Studies developing or validating AI models for ONFH diagnosis using imaging data were eligible. Risk of bias was assessed using the QUADAS-2 tool. Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were pooled using a bivariate mixed-effects model, and a summary receiver operating characteristic (SROC) curve was constructed. Subgroup analyses were stratified by imaging modality (x-ray vs MRI), disease stage (early-stage ONFH vs all-stage ONFH), diagnostic criteria (Association Research Circulation Osseous [ARCO] staging vs other criteria), control group type (healthy controls vs disease controls), validation method (internal validation vs external validation), center type (single-center vs multicenter), and model type (deep learning vs machine learning). Meta-regression was performed to quantify the contribution of each covariate to between-study heterogeneity. Sensitivity analysis and Deeks asymmetry test assessed the robustness of the results and publication bias. Clinical utility was evaluated using the Fagan nomogram. RESULTS: A total of 12 studies comprising 16,189 hip joints were included. The pooled sensitivity was 0.91 (95% CI 0.87-0.95), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the SROC AUC was 0.97 (95% CI 0.95-0.98). Substantial between-study heterogeneity was observed (²=72%, 95% CI 38%-100%). Subgroup analysis showed that MRI-based models yielded a higher diagnostic odds ratio (DOR; 382, 95% CI 220-665) than x-ray-based models (106, 95% CI 60-190), while models that underwent external validation had a lower DOR (129, 95% CI 51-329) than those with only internal validation (230, 95% CI 104-510). Meta-regression identified imaging modality as the primary source of heterogeneity, explaining 92.1% of the between-study variance. CONCLUSIONS: AI models demonstrate high diagnostic accuracy in imaging-based ONFH diagnosis. However, the current evidence is constrained by the limited number of included studies, predominantly retrospective designs, and a lack of adequate external validation, and should therefore be interpreted with caution. Future research should adopt multicenter prospective designs, standardize reference standards, and implement rigorous external validation to facilitate clinical translation.