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Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review

Published:September 18, 2020DOI:https://doi.org/10.1016/j.arthro.2020.09.012

      Purpose

      To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the efficacy to human clinical experts.

      Methods

      PubMed, OVID/Medline, and Cochrane libraries were queried in November 2019 for research articles pertaining to AI use for detection of ACL and meniscus tears. Information regarding AI model, prediction accuracy/area under the curve (AUC), sample sizes of testing/training sets, and imaging modalities were recorded.

      Results

      A total of 11 AI studies were identified: 5 investigated ACL tears, 5 investigated meniscal tears, and 1 investigated both. The AUC of AI models for detecting ACL tears ranged from 0.895 to 0.980, and the prediction accuracy ranged from 86.7% to 100%. Of these studies, 3 compared AI models to clinical experts. Two found no significant differences in diagnostic capability, whereas one found that radiologists had a significantly greater sensitivity for detecting ACL tears (P = .002) and statistically similar specificity and accuracy. Of the 5 studies investigating the meniscus, the AUC for AI models ranged from 0.847 to 0.910 and prediction accuracy ranged from 75.0% to 90.0%. Of these studies, 2 compared AI models with clinical experts. One found no significant differences in diagnostic accuracy, whereas one found that the AI model had a significantly lower specificity (P = .003) and accuracy (P = .015) than radiologists. Two studies reported that the addition of AI models significantly increased the diagnostic performance of clinicians compared to their efforts without these models.

      Conclusions

      AI prediction capabilities were excellent and may enhance the diagnosis of ACL and meniscal pathology; however, AI did not outperform clinical experts.

      Clinical Relevance

      AI models promise to improve diagnosing certain pathologies as well as or better than human experts, are excellent for detecting ACL and meniscus tears, and may enhance the diagnostic capabilities of human experts; however, when compared with these experts, they may not offer any significant advantage.
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