Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance

• Published in: PloS one Vol. 20; no. 6; p. e0326339
• Main Authors: Mohammadi, Soheil, Jahanshahi, Ali, Shahrabi Farahani, Mohammad, Salehi, Mohammad Amin, Frounchi, Negin, Guermazi, Ali
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.06.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bias; Computer and Information Sciences; Confidence intervals; Contingency; Contingency tables; Data augmentation; Deep learning; Diagnosis; Diagnostic tests; Electronic health records; Health aspects; Humans; Injuries; Injury analysis; Knee; Knee Injuries - diagnosis; Machine learning; Magnetic resonance imaging; Medicine and Health Sciences; Meniscus; Meta-analysis; Orthopedics; Osteoarthritis; People and Places; Performance measurement; Physical Sciences; Prediction models; Regression analysis; Research and Analysis Methods; Science Policy; Sensitivity and Specificity; Statistical analysis; Systematic review; Tables (data); Tibial Meniscus Injuries - diagnosis; Tibial Meniscus Injuries - diagnostic imaging; Transfer learning; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0326339

The aim was to systematically review the literature and perform a meta-analysis to estimate the performance of artificial intelligence (AI) algorithms in detecting meniscal injuries. A systematic search was performed in the Scopus, PubMed, EBSCO, Cinahl, Web of Science, IEEE Xplore, and Cochrane Central databases on July, 2024. The included studies' reporting quality and risk of bias were evaluated using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and the Prediction Model Study Risk of Bias Assessment Tool (PROBAST), respectively. Also, a meta-analysis was done using contingency tables to estimate diagnostic performance metrics (sensitivity and specificity), and a meta-regression analysis was performed to investigate the effect of the following variables on the main outcome: imaging view, data augmentation and transfer learning usage, and presence of meniscal tear in the injury, with a corresponding 95% confidence interval (CI) and a P-value of 0.05 as a threshold for significance. Among 28 included studies, 92 contingency tables were extracted from 15 studies. The reference standard of the studies were mostly expert radiologists, orthopedics, or surgical reports. The pooled sensitivity and specificity for AI algorithms on internal validation were 81% (95% CI: 78, 85), and 78% (95% CI: 72, 83), and for clinicians on internal validation were 85% (95% CI: 76, 91), and 88% (95% CI: 83, 92), respectively. The pooled sensitivity and specificity for studies validating algorithms with an external test set were 82% (95% CI: 74, 88), and 88% (95% CI: 84, 91), respectively. The results of this study imply the lower diagnostic performance of AI-based algorithms in knee meniscal injuries compared with clinicians.