{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:42:46Z","timestamp":1781268166785,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&amp;DL and logistic regression methods. Affecting factors of the logistic regression method corresponded well with the feature importance of the six top-ranked factors in the ML&amp;DL method. There was no significant difference when comparing the accuracy, F1-score, and error rate between ML&amp;DL and logistic regression methods (accuracy = 0.89 and 0.91, F1 score = 0.89 and 0.90, error rate = 0.11 and 0.09; p = 0.114, 0.422, and 0.119, respectively). The area under the curve (AUC) values showed excellent test quality for both ML&amp;DL and logistic regression methods (AUC = 0.97 and 0.94, respectively) in the evaluation of prediction performance (p = 0.289). The affecting factors of the logistic regression method and the influence of the ML&amp;DL method were not significantly different. The accuracy and performance of the ML&amp;DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&amp;DL algorithm could potentially predict the outcome of the MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice.<\/jats:p>","DOI":"10.3390\/diagnostics11071225","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"1225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression, Gradient Boosting, and CNN Algorithms"],"prefix":"10.3390","volume":"11","author":[{"given":"Jae-Ik","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4246-8803","authenticated-orcid":false,"given":"Dong-Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5979-1765","authenticated-orcid":false,"given":"Hyun-Jin","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9162-0560","authenticated-orcid":false,"given":"Han-Gyeol","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Seuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.2105\/AJPH.84.3.351","article-title":"The effects of specific medical conditions on the functional limitations of elders in the Framingham Study","volume":"84","author":"Guccione","year":"1994","journal-title":"Am. J. Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1002\/1529-0131(199808)41:8<1343::AID-ART3>3.0.CO;2-9","article-title":"An update on the epidemiology of knee and hip osteoarthritis with a view to prevention","volume":"41","author":"Felson","year":"1998","journal-title":"Arthritis Rheumatism"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1007\/s11999-015-4450-3","article-title":"Prevalence and Risk Factors of Spine, Shoulder, Hand, Hip, and Knee Osteoarthritis in Community-dwelling Koreans Older Than Age 65 Years","volume":"473","author":"Cho","year":"2015","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1002\/art.24383","article-title":"Meniscal tear in knees without surgery and the development of radiographic osteoarthritis among middle-aged and elderly persons: The Multicenter Osteoarthritis Study","volume":"60","author":"Englund","year":"2009","journal-title":"Arthritis Rheumatism"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.arthro.2004.01.004","article-title":"Radial tears of the posterior horn of the medial meniscus","volume":"20","author":"Bin","year":"2004","journal-title":"Arthrosc. J. Arthrosc. Relat. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1302\/0301-620X.81B1.0810037","article-title":"Meniscal movement: An in-vivo study using dynamic MRI","volume":"81","author":"Vedi","year":"1999","journal-title":"J. Bone Jt. Surg. Br. Vol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Analysis"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep Learning\u2014A Technology With the Potential to Transform Health Care","volume":"320","author":"Hinton","year":"2018","journal-title":"JAMA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep Learning in Medical Image Analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.knee.2016.12.004","article-title":"The role of medial meniscus posterior root tear and proximal tibial morphology in the development of spontaneous osteonecrosis and osteoarthritis of the knee","volume":"24","author":"Yamagami","year":"2017","journal-title":"Knee"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1016\/j.otsr.2017.07.022","article-title":"Medial meniscus extrusion correlates with disease duration of the sudden symptomatic medial meniscus posterior root tear","volume":"103","author":"Furumatsu","year":"2017","journal-title":"Orthop. Traumatol. Surg. Res. OTSR"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.5792\/ksrr.2015.27.2.90","article-title":"Clinical Evaluation of the Root Tear of the Posterior Horn of the Medial Meniscus in Total Knee Arthroplasty for Osteoarthritis","volume":"27","author":"Choi","year":"2015","journal-title":"Knee Surg. Relat. Res."},{"key":"ref_14","first-page":"1","article-title":"Bone Marrow Lesion: Image, Clinical Presentation, and Treatment","volume":"10","author":"Bonadio","year":"2017","journal-title":"Magn. Reson. Insights"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1007\/s00167-008-0708-6","article-title":"Varus inclination of the proximal tibia or the distal femur does not influence high tibial osteotomy outcome","volume":"17","author":"Takacs","year":"2009","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1007\/s11999-016-4732-4","article-title":"Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis","volume":"474","author":"Kohn","year":"2016","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.joca.2011.05.004","article-title":"Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score)","volume":"19","author":"Hunter","year":"2011","journal-title":"Osteoarthr. Cartilage"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kubben, P., Dumontier, M., and Dekker, A. (2019). Prediction Modeling Methodology. Fundamentals of Clinical Data Science, Springer.","DOI":"10.1007\/978-3-319-99713-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.amepre.2005.01.013","article-title":"Associations of body mass index with meniscal tears","volume":"28","author":"Ford","year":"2005","journal-title":"Am. J. Prev. Med."},{"key":"ref_20","first-page":"51","article-title":"Factors associated with radiographic osteoarthritis of the knee among community-dwelling Japanese women: The Hizen-Oshima Study","volume":"16","author":"Hashikawa","year":"2011","journal-title":"J. Orthop. Sci. Off. J. Jpn. Orthop. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.csm.2019.02.010","article-title":"Degenerative Meniscal Tears and High Tibial Osteotomy: Do Current Treatment Algorithms Need to Be Realigned?","volume":"38","author":"Primeau","year":"2019","journal-title":"Clin. Sports Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/jor.21242","article-title":"Effect of posterior tibial slope on knee biomechanics during functional activity","volume":"29","author":"Shelburne","year":"2010","journal-title":"J. Orthop. Res. Off. Publ. Orthop. Res. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","article-title":"A method of comparing the areas under receiver operating characteristic curves derived from the same cases","volume":"148","author":"Hanley","year":"1983","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15415","DOI":"10.1038\/s41598-017-15720-y","article-title":"Searching for prostate cancer by fully automated magnetic resonance imaging classification: Deep learning versus non-deep learning","volume":"1","author":"Wang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1148\/radiol.2018172986","article-title":"Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection","volume":"289","author":"Liu","year":"2018","journal-title":"Radiology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e200265","DOI":"10.1001\/jamanetworkopen.2020.0265","article-title":"Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms","volume":"3","author":"Schaffter","year":"2020","journal-title":"JAMA Netw. Open"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/7\/1225\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:05Z","timestamp":1760164025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/7\/1225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":26,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["diagnostics11071225"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11071225","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}