{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:07:13Z","timestamp":1790698033268,"version":"4.1.0"},"reference-count":65,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Anterior cruciate ligament (ACL) tears are among the most common knee injuries, accounting for half of all knee ligament injuries. Magnetic resonance imaging (MRI) is the standard for diagnosing ACL tears, but its interpretation is experience\u2010dependent. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a transformative tool in medical imaging, offering advanced pattern recognition for detecting abnormalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study systematically reviews and meta\u2010analyses the diagnostic accuracy of AI in detecting ACL tears, comparing with radiologists' performance. Following Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses of Diagnostic Test Accuracy Studies (PRISMA\u2010DTA) guidelines, a comprehensive search of Embase, PubMed and Cochrane databases identified 247 articles, with 40 studies included in qualitative synthesis and 31 in meta\u2010analysis, encompassing 1192 cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the overall pooled analysis, AI demonstrated a sensitivity of 0.94 (95% confidence interval [CI]: 0.93\u20130.95) and specificity of 0.93 (95% CI: 0.92\u20130.93). In the subgroup comparing AI directly to radiologists, AI demonstrated pooled sensitivity and specificity of 0.90 (95% CI: 0.88\u20130.92) and 0.91 (95% CI: 0.89\u20130.92), respectively, while radiologists showed 0.85 (95% CI: 0.83\u20130.87) and 0.90 (95% CI: 0.88\u20130.91). Heterogeneity varied, with moderate to high heterogeneity in the analyses. Summary receiver operating characteristic (sROC) analysis indicated no significant difference between AI and radiologists in diagnostic accuracy (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.782).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest that AI can match human diagnostic performance for ACL tears, offering advantages such as reduced costs, faster results and decreased physician burden. Despite variability in study settings, AI shows promise as a reliable diagnostic tool in knee imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>Level II.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ksa.70478","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:42:56Z","timestamp":1782848576000},"page":"3816-3828","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence demonstrates comparable diagnostic accuracy to radiologists for anterior cruciate ligament tears on MRI: A systematic review and meta\u2010analysis"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2215-779X","authenticated-orcid":false,"given":"Gabriel","family":"Moraes de Oliveira","sequence":"first","affiliation":[{"name":"Paulista School of Medicine Federal University of Sao Paulo (EPM\u2010UNIFESP) S\u00e3o Paulo Brazil"},{"name":"Massachusetts General Hospital Harvard Medical School Boston USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0792-8631","authenticated-orcid":false,"given":"Serafina","family":"Zotter","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School Boston USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5725-5745","authenticated-orcid":false,"given":"Brian","family":"Tao","sequence":"additional","affiliation":[{"name":"Boston University Chobanian &amp; Averdisian School of Medicine Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3405-1707","authenticated-orcid":false,"given":"Ana Beatriz Nardelli da","family":"Silva","sequence":"additional","affiliation":[{"name":"University of Bradford Bradford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2604-1372","authenticated-orcid":false,"given":"Maria","family":"Mascarenhas","sequence":"additional","affiliation":[{"name":"Santa Casa de S\u00e3o Paulo School of Medical Sciences S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8386-3121","authenticated-orcid":false,"given":"Chilan","family":"B. G. Leite","sequence":"additional","affiliation":[{"name":"Brigham and Women's Hospital, Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9716-551X","authenticated-orcid":false,"given":"Guilherme","family":"Almeida","sequence":"additional","affiliation":[{"name":"Federal University of Sergipe Aracaju Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1067-727X","authenticated-orcid":false,"given":"Nacime","family":"Mansur","sequence":"additional","affiliation":[{"name":"Paulista School of Medicine Federal University of Sao Paulo (EPM\u2010UNIFESP) S\u00e3o Paulo Brazil"},{"name":"Medstar Union Memorial Hospital Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-9640","authenticated-orcid":false,"given":"Giovanna","family":"Medina","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital Harvard Medical School Boston USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"issue":"3","key":"e_1_2_12_2_1","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2021200165","article-title":"Automatic deep learning\u2013assisted detection and grading of abnormalities in knee MRI studies","volume":"3","author":"Astuto B","year":"2021","journal-title":"Radiol Artif Intell"},{"issue":"2","key":"e_1_2_12_3_1","doi-asserted-by":"crossref","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","article-title":"Artificial intelligence in healthcare: transforming the practice of medicine","volume":"8","author":"Bajwa J","year":"2021","journal-title":"Future Healthc J"},{"issue":"5","key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2519\/jospt.2006.2011","article-title":"Clinical diagnosis of an anterior cruciate ligament rupture: a meta\u2010analysis","volume":"36","author":"Benjaminse A","year":"2006","journal-title":"J Orthop Sports Phys Ther"},{"issue":"11","key":"e_1_2_12_5_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002699","article-title":"Deep\u2010learning\u2010assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet","volume":"15","author":"Bien N","year":"2018","journal-title":"PLoS Med"},{"issue":"1","key":"e_1_2_12_6_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbspin.2022.105493","article-title":"Application of artificial intelligence to imaging interpretations in the musculoskeletal area: where are we? Where are we going?","volume":"90","author":"Bousson V","year":"2023","journal-title":"Joint Bone Spine"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106309","article-title":"Improved anterior cruciate ligament tear diagnosis using gated recurrent unit networks and hybrid Tasmanian devil optimization","volume":"95","author":"Chan S","year":"2024","journal-title":"Biomed Signal Process Control"},{"issue":"6","key":"e_1_2_12_8_1","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1007\/s10278-019-00193-4","article-title":"Deep learning for detection of complete anterior cruciate ligament tear","volume":"32","author":"Chang PD","year":"2019","journal-title":"J Digit Imaging"},{"key":"e_1_2_12_9_1","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.2147\/RMHP.S312330","article-title":"A novel application of unsupervised machine learning and supervised machine learning\u2010derived radiomics in anterior cruciate ligament rupture","volume":"14","author":"Chen D\u2010S","year":"2021","journal-title":"Risk Manag Healthc Policy"},{"issue":"6","key":"e_1_2_12_10_1","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s11604-022-01385-9","article-title":"A transfer learning approach for staging diagnosis of anterior cruciate ligament injury on a new modified MR dual precision positioning of thin\u2010slice oblique sagittal FS\u2010PDWI sequence","volume":"41","author":"Chen J","year":"2023","journal-title":"Jpn J Radiol"},{"issue":"1","key":"e_1_2_12_11_1","doi-asserted-by":"crossref","DOI":"10.2196\/37508","article-title":"Artificial intelligence\u2013assisted diagnosis of anterior cruciate ligament tears from magnetic resonance images: algorithm development and validation study","volume":"1","author":"Chen K\u2010H","year":"2022","journal-title":"JMIR AI"},{"issue":"1","key":"e_1_2_12_12_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/s13018-024-04602-5","article-title":"Application of machine learning\u2010based multi\u2010sequence MRI radiomics in diagnosing anterior cruciate ligament tears","volume":"19","author":"Cheng Q","year":"2024","journal-title":"J Orthop Surg"},{"issue":"8","key":"e_1_2_12_13_1","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1177\/0363546517730072","article-title":"High rates of osteoarthritis develop after anterior cruciate ligament surgery: an analysis of 4108 patients","volume":"46","author":"Cinque ME","year":"2018","journal-title":"Am J Sports Med"},{"issue":"7","key":"e_1_2_12_14_1","doi-asserted-by":"crossref","DOI":"10.1177\/23259671211014206","article-title":"Artificial intelligence in the management of anterior cruciate ligament injuries","volume":"9","author":"Corban J","year":"2021","journal-title":"Orthop J Sports Med"},{"issue":"9","key":"e_1_2_12_15_1","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks JJ","year":"2005","journal-title":"J Clin Epidemiol"},{"issue":"3","key":"e_1_2_12_16_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.diii.2022.10.010","article-title":"End\u2010to\u2010end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI","volume":"104","author":"Dung NT","year":"2023","journal-title":"Diagn Interv Imaging"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2022.102142"},{"issue":"2","key":"e_1_2_12_18_1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s00256-021-03830-8","article-title":"Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state\u2010of\u2010the\u2010art of deep learning\u2010based approaches","volume":"51","author":"Fritz B","year":"2022","journal-title":"Skeletal Radiol"},{"issue":"1","key":"e_1_2_12_19_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1055\/s-0039-3400264","article-title":"The use of artificial intelligence in the evaluation of knee pathology","volume":"24","author":"Garwood ER","year":"2020","journal-title":"Semin Musculoskelet Radiol"},{"issue":"8","key":"e_1_2_12_20_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1097\/RLI.0000000000000664","article-title":"Deep convolutional neural network\u2013based diagnosis of anterior cruciate ligament tears","volume":"55","author":"Germann C","year":"2020","journal-title":"Invest Radiol"},{"issue":"3","key":"e_1_2_12_21_1","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.jacr.2017.12.028","article-title":"Machine learning in medical imaging","volume":"15","author":"Giger ML","year":"2018","journal-title":"J Am Coll Radiol"},{"issue":"4","key":"e_1_2_12_22_1","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1007\/s00330-025-12020-2","article-title":"AI demonstrates comparable diagnostic performance to radiologists in MRI detection of anterior cruciate ligament tears: a systematic review and meta\u2010analysis","volume":"36","author":"Gill SS","year":"2026","journal-title":"Eur Radiol"},{"issue":"2","key":"e_1_2_12_23_1","first-page":"151","article-title":"Artificial intelligence in anterior cruciate ligament tear diagnosis: a bibliometric analysis of the 50 most cited studies","volume":"36","author":"Gill SS","year":"2025","journal-title":"Indian J Radiol Imaging"},{"key":"e_1_2_12_24_1","doi-asserted-by":"crossref","DOI":"10.2174\/0115734056295157240418043624","article-title":"A systematic review on deep learning model in computer\u2010aided diagnosis for anterior cruciate ligament injury","volume":"20","author":"Herman H","year":"2024","journal-title":"Curr Med Imaging Rev"},{"issue":"1","key":"e_1_2_12_25_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3390\/diagnostics11010105","article-title":"Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach","volume":"11","author":"Javed Awan M","year":"2021","journal-title":"Diagnostics"},{"issue":"7","key":"e_1_2_12_26_1","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1109\/JBHI.2021.3081355","article-title":"Interpretable and lightweight 3\u2010D deep learning model for automated ACL diagnosis","volume":"25","author":"Jeon Y","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_2_12_27_1","first-page":"7432","article-title":"Anterior cruciate ligament tear detection in mri images using multi\u2010neighbor local binary pattern","author":"Joshi K","year":"2022","journal-title":"J Pharm Negat Results"},{"issue":"10","key":"e_1_2_12_28_1","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.3390\/diagnostics12102314","article-title":"Anterior cruciate ligament tear detection based on deep convolutional neural network","volume":"12","author":"Joshi K","year":"2022","journal-title":"Diagnostics"},{"issue":"1","key":"e_1_2_12_29_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csm.2016.08.001","article-title":"Epidemiology and diagnosis of anterior cruciate ligament injuries","volume":"36","author":"Kaeding CC","year":"2017","journal-title":"Clin Sports Med"},{"issue":"12","key":"e_1_2_12_30_1","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1007\/s00256-022-04081-x","article-title":"Ensemble deep learning model for predicting anterior cruciate ligament tear from lateral knee radiograph","volume":"51","author":"Kim DH","year":"2022","journal-title":"Skeletal Radiol"},{"issue":"6","key":"e_1_2_12_31_1","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s00330-009-1298-5","article-title":"MRI of the knee: how do field strength and radiologist's experience influence diagnostic accuracy and interobserver correlation in assessing chondral and meniscal lesions and the integrity of the anterior cruciate ligament?","volume":"19","author":"Krampla W","year":"2009","journal-title":"Eur Radiol"},{"issue":"50","key":"e_1_2_12_32_1","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000018324","article-title":"Diagnostic accuracy of machine\u2010learning\u2010assisted detection for anterior cruciate ligament injury based on magnetic resonance imaging","volume":"98","author":"Lao Y","year":"2019","journal-title":"Medicine"},{"key":"e_1_2_12_33_1","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1268543","article-title":"Automated diagnosis of anterior cruciate ligament via a weighted multi\u2010view network","volume":"11","author":"Li F","year":"2023","journal-title":"Front Bioeng Biotechnol"},{"key":"e_1_2_12_34_1","first-page":"1","article-title":"Deep learning\u2010based magnetic resonance imaging image features for diagnosis of anterior cruciate ligament injury","volume":"2021","author":"Li Z","year":"2021","journal-title":"J Healthc Eng"},{"issue":"1","key":"e_1_2_12_35_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1186\/s12880-023-01091-6","article-title":"Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules","volume":"23","author":"Liang C","year":"2023","journal-title":"BMC Med Imaging"},{"issue":"3","key":"e_1_2_12_36_1","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2019180091","article-title":"Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning","volume":"1","author":"Liu F","year":"2019","journal-title":"Radiol Artif Intell"},{"issue":"5","key":"e_1_2_12_37_1","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.210315","article-title":"RadImageNet: an open radiologic deep learning research dataset for effective transfer learning","volume":"4","author":"Mei X","year":"2022","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"e_1_2_12_38_1","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1186\/s12891-022-05524-1","article-title":"Automated detection of anterior cruciate ligament tears using a deep convolutional neural network","volume":"23","author":"Minamoto Y","year":"2022","journal-title":"BMC Musculoskelet Disord"},{"issue":"4","key":"e_1_2_12_39_1","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190207","article-title":"Deep learning for hierarchical severity staging of anterior cruciate ligament injuries from MRI","volume":"2","author":"Namiri NK","year":"2020","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"e_1_2_12_40_1","doi-asserted-by":"crossref","first-page":"7637","DOI":"10.1038\/s41467-024-51888-4","article-title":"Learning co\u2010plane attention across MRI sequences for diagnosing twelve types of knee abnormalities","volume":"15","author":"Qiu Z","year":"2024","journal-title":"Nat Commun"},{"issue":"2","key":"e_1_2_12_41_1","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1067\/j.cpradiol.2019.10.004","article-title":"MR protocol optimization with deep learning: a proof of concept","volume":"50","author":"Richardson ML","year":"2021","journal-title":"Curr Probl Diagn Radiol"},{"key":"e_1_2_12_42_1","doi-asserted-by":"crossref","first-page":"m2632","DOI":"10.1136\/bmj.m2632","article-title":"Preferred reporting items for systematic review and meta\u2010analysis of diagnostic test accuracy studies (PRISMA\u2010DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh J\u2010P","year":"2020","journal-title":"BMJ"},{"issue":"2","key":"e_1_2_12_43_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/00913847.2015.1016865","article-title":"State\u2010of\u2010the\u2010art anterior cruciate ligament tears: a primer for primary care physicians","volume":"43","author":"Salzler M","year":"2015","journal-title":"Phys Sportsmed"},{"issue":"5","key":"e_1_2_12_44_1","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/j.acra.2022.07.011","article-title":"Academic radiology departments should lead artificial intelligence initiatives","volume":"30","author":"Santomartino SM","year":"2023","journal-title":"Acad Radiol"},{"issue":"3","key":"e_1_2_12_45_1","doi-asserted-by":"crossref","DOI":"10.2196\/10010","article-title":"Artificial intelligence versus clinicians in disease diagnosis: systematic review","volume":"7","author":"Shen J","year":"2019","journal-title":"JMIR Med Inform"},{"issue":"44","key":"e_1_2_12_46_1","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000031510","article-title":"Development of convolutional neural network model for diagnosing tear of anterior cruciate ligament using only one knee magnetic resonance image","volume":"101","author":"Shin H","year":"2022","journal-title":"Medicine"},{"key":"e_1_2_12_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102424"},{"issue":"3","key":"e_1_2_12_48_1","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1148\/radiol.2018180547","article-title":"Convolutional neural networks for radiologic images: a radiologist's guide","volume":"290","author":"Soffer S","year":"2019","journal-title":"Radiology"},{"key":"e_1_2_12_49_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/7872500","article-title":"A torn ACL mapping in knee MRI images using deep convolution neural network with inception\u2010v3","volume":"2022","author":"Sridhar S","year":"2022","journal-title":"J Healthc Eng"},{"issue":"1","key":"e_1_2_12_50_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1302\/0301-620X.95B1.29636","article-title":"The incidence of secondary pathology after anterior cruciate ligament rupture in 5086 patients requiring ligament reconstruction","volume":"95","author":"Sri\u2010Ram K","year":"2013","journal-title":"Bone Joint J"},{"key":"e_1_2_12_51_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.cmpb.2016.12.006","article-title":"Semi\u2010automated detection of anterior cruciate ligament injury from MRI","volume":"140","author":"\u0160tajduhar I","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"key":"e_1_2_12_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105019"},{"issue":"12","key":"e_1_2_12_53_1","doi-asserted-by":"crossref","first-page":"8394","DOI":"10.1007\/s00330-022-08923-z","article-title":"Deep learning to detect anterior cruciate ligament tear on knee MRI: multi\u2010continental external validation","volume":"32","author":"Tran A","year":"2022","journal-title":"Eur Radiol"},{"issue":"4","key":"e_1_2_12_54_1","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1016\/j.arthro.2023.08.010","article-title":"A deep learning model enhances clinicians' diagnostic accuracy to more than 96% for anterior cruciate ligament ruptures on magnetic resonance imaging","volume":"40","author":"Wang DY","year":"2024","journal-title":"Arthroscopy"},{"issue":"1","key":"e_1_2_12_55_1","doi-asserted-by":"crossref","first-page":"9843","DOI":"10.1038\/s41598-024-60419-6","article-title":"Improving inceptionV4 model based on fractional\u2010order snow leopard optimization algorithm for diagnosing of ACL tears","volume":"14","author":"Wang D","year":"2024","journal-title":"Sci Rep"},{"issue":"2","key":"e_1_2_12_56_1","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1007\/s10278-023-00944-4","article-title":"Lightweight attentive graph neural network with conditional random field for diagnosis of anterior cruciate ligament tear","volume":"37","author":"Wang J","year":"2024","journal-title":"J Imaging Inform Med"},{"issue":"5","key":"e_1_2_12_57_1","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.21037\/qims-23-1539","article-title":"One\u2010stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation","volume":"14","author":"Wang M","year":"2024","journal-title":"Quant Imaging Med Surg"},{"issue":"8","key":"e_1_2_12_58_1","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.3390\/tomography10080094","article-title":"Deep Learning\u2010assisted automatic diagnosis of anterior cruciate ligament tear in knee magnetic resonance images","volume":"10","author":"Wang X","year":"2024","journal-title":"Tomography"},{"issue":"8","key":"e_1_2_12_59_1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS\u20102: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting PF","year":"2011","journal-title":"Ann Intern Med"},{"issue":"1","key":"e_1_2_12_60_1","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1038\/s41598-024-51666-8","article-title":"Approaching expert\u2010level accuracy for differentiating ACL tear types on MRI with deep learning","volume":"14","author":"Xue Y","year":"2024","journal-title":"Sci Rep"},{"key":"e_1_2_12_61_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","article-title":"Convolutional neural networks for medical image analysis: state\u2010of\u2010the\u2010art, comparisons, improvement and perspectives","volume":"444","author":"Yu H","year":"2021","journal-title":"Neurocomputing"},{"issue":"6","key":"e_1_2_12_62_1","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1002\/jmri.27266","article-title":"Deep learning approach for anterior cruciate ligament lesion detection: evaluation of diagnostic performance using arthroscopy as the reference standard","volume":"52","author":"Zhang L","year":"2020","journal-title":"J Magn Reson Imaging"},{"key":"e_1_2_12_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105697"},{"issue":"9","key":"e_1_2_12_64_1","doi-asserted-by":"crossref","first-page":"5954","DOI":"10.1007\/s00330-024-10625-7","article-title":"Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta\u2010analysis","volume":"34","author":"Zhao Y","year":"2024","journal-title":"Eur Radiol"},{"issue":"5","key":"e_1_2_12_65_1","doi-asserted-by":"crossref","first-page":"2952","DOI":"10.1007\/s00330-024-11154-z","article-title":"Correction: artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta\u2010analysis","volume":"35","author":"Zhao Y","year":"2024","journal-title":"Eur Radiol"},{"key":"e_1_2_12_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106616"}],"container-title":["Knee Surgery, Sports Traumatology, Arthroscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ksa.70478","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ksa.70478","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ksa.70478","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:39:21Z","timestamp":1790696361000},"score":1,"resource":{"primary":{"URL":"https:\/\/esskajournals.onlinelibrary.wiley.com\/doi\/10.1002\/ksa.70478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":65,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10.1002\/ksa.70478"],"URL":"https:\/\/doi.org\/10.1002\/ksa.70478","archive":["Portico"],"relation":{},"ISSN":["0942-2056","1433-7347"],"issn-type":[{"value":"0942-2056","type":"print"},{"value":"1433-7347","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-01-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}