Kyle N Kunze1, David M Rossi2, Gregory M White3, Aditya V Karhade4, Jie Deng3, Brady T Williams2, Jorge Chahla5. 1. Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A. 2. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A. 3. Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, U.S.A. 4. Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A. 5. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A.. Electronic address: jachahla@msn.com.
Abstract
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.
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.