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Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (\n                      <jats:italic>I<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200979%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80\u20130.91) and 0.89 (95% CI 0.83\u20130.93) for meniscus tear identification and 0.88 (95% CI 0.82\u20130.91) and 0.84 (95% CI 0.81\u20130.85) for locating the tears.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance statement<\/jats:title>\n                    <jats:p>Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:italic>\u2022<\/jats:italic>\n                      <jats:italic>Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022<\/jats:italic>\n                      <jats:italic>The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%).<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022<\/jats:italic>\n                      <jats:italic>AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-024-10625-7","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T07:18:20Z","timestamp":1708586300000},"page":"5954-5964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4563-4344","authenticated-orcid":false,"given":"Yi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-9876","authenticated-orcid":false,"given":"Andrew","family":"Coppola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7571-2059","authenticated-orcid":false,"given":"Urvi","family":"Karamchandani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitri","family":"Amiras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1317-4512","authenticated-orcid":false,"given":"Chinmay M.","family":"Gupte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"issue":"2","key":"10625_CR1","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/s0749-8063(97)90158-4","volume":"13","author":"LM White","year":"1997","unstructured":"White LM, Schweitzer ME, Deely DM, Morrison WB (1997) The effect of training and experience on the magnetic resonance imaging interpretation of meniscal tears. 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No products from the company were used in this study. DA was not involved in the control of the data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because this is a systematic review and meta-analysis with data extracted from publications that have already obtained consent for their study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was not required because this is a systematic review.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"None.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 diagnostic study\u2022 multicenter study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}