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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Article info
Publication history
Published online: September 18, 2020
Accepted:
September 9,
2020
Received:
April 5,
2020
See commentary on page 782Footnotes
The authors report that they have no conflicts of interest in the authorship and publication of this article. Full ICMJE author disclosure forms are available for this article online, as supplementary material.
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© 2020 by the Arthroscopy Association of North America
