人工智能在MRI诊断前交叉韧带撕裂方面与放射科医生准确性相当：一项系统评价与Meta分析
简介
这项系统综述与荟萃分析纳入了31项研究（1192例），比较人工智能（AI）与放射科医生在MRI上诊断前交叉韧带（ACL）撕裂的准确性。结果显示，AI的汇总敏感度（0.94）和特异度（0.93）均较高；直接对比亚组中，AI的敏感度（0.90）和特异度（0.91）与放射科医生（0.85/0.90）无显著差异（sROC分析p=0.782）。提示AI可作为可靠的辅助…
英文摘要
PURPOSE: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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 OF EVIDENCE: Level II.