Artificial intelligence demonstrates comparable diagnostic accuracy to radiologists for anterior cruciate ligament tears on MRI: A systematic review and meta-analysis

Anterior cruciate ligament (ACL) tears are among the most common knee injuries, accounting for half of all knee ligament injuries. Magnetic resonance imaging (MRI) is the standard for diagnosing ACL tears, but its interpretation is experience-dependent. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a transformative tool in medical imaging, offering advanced pattern recognition for detecting abnormalities. This study systematically reviews and meta-analyses the diagnostic accuracy of AI in detecting ACL tears, comparing with radiologists' performance. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines, a comprehensive search of Embase, PubMed and Cochrane databases identified 247 articles, with 40 studies included in qualitative synthesis and 31 in meta-analysis, encompassing 1192 cases. In the overall pooled analysis, AI demonstrated a sensitivity of 0.94 (95% confidence interval [CI]: 0.93-0.95) and specificity of 0.93 (95% CI: 0.92-0.93). In the subgroup comparing AI directly to radiologists, AI demonstrated pooled sensitivity and specificity of 0.90 (95% CI: 0.88-0.92) and 0.91 (95% CI: 0.89-0.92), respectively, while radiologists showed 0.85 (95% CI: 0.83-0.87) and 0.90 (95% CI: 0.88-0.91). Heterogeneity varied, with moderate to high heterogeneity in the analyses. Summary receiver operating characteristic (sROC) analysis indicated no significant difference between AI and radiologists in diagnostic accuracy (p = 0.782). These findings suggest that AI can match human diagnostic performance for ACL tears, offering advantages such as reduced costs, faster results and decreased physician burden. Despite variability in study settings, AI shows promise as a reliable diagnostic tool in knee imaging. Level II.