[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124950-en":3,"doc-seo-124950-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124950,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging","Knee injuries significantly affect quality of life, and interpreting magnetic resonance imaging (MRI) remains time-consuming for radiologists due to the high level of detail. This study presents a machine-learning model using convolutional neural networks to detect medial meniscus tears, bone marrow edema, and general abnormalities in knee MRI with a real-life imaging protocol. Performance is assessed using accuracy, sensitivity, and specificity, achieving up to 83.7% accuracy, 82.2% sensitivity, and 87.99% specificity for meniscus tears, with similarly strong results for bone marrow edema and general abnormalities.","Article  \nA Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging  \nMassimiliano Mangone 1,*, Anxhelo Diko 1,2, Luca Giuliani 3, Francesco Agostini 1, Marco Paoloni 1, Andrea Bernetti 1, Gabriele Santilli 1, Marco Conti 1, Alessio Savina 1, Giovanni Iudicelli 1, Carlo Ottonello 4 and Valter Santilli 1  \nCitation: Mangone, M.; Diko, A.; Giuliani, L.; Agostini, F.; Paoloni, M.; Bernetti, A.; Santilli, G.; Conti, M.; Savina, A.; Iudicelli, G.; et al. A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging. Int. J. Environ. Res. Public Health 2023, 20, 6059 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijerph20126059  \nAcademic Editors: Keun Ho Ryu and Nipon Theera-Umpon  \nReceived: 5 May 2023  \nRevised: 27 May 2023  \nAccepted: 5 June 2023  \nPublished: 6 June 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University,  \n00185 Rome, Italy; [anxhelo.diko@uniroma1.it](anxhelo.diko@uniroma1.it) (A.D.); [francescoagostini.ff@gmail.com](francescoagostini.ff@gmail.com) (F.A.);  \n[marco.paoloni@uniroma1.it](marco.paoloni@uniroma1.it) (M.P.); [andrea.bernetti@uniroma1.it](andrea.bernetti@uniroma1.it) (A.B.); [gabriele.santilli@uniroma1.it](gabriele.santilli@uniroma1.it) (G.S.);  \n[ma.conti@uniroma1.it](ma.conti@uniroma1.it) (M.C.); [alessio.savina@uniroma1.it](alessio.savina@uniroma1.it) (A.S.); [giovanni.iudicelli@uniroma1.it](giovanni.iudicelli@uniroma1.it) (G.I.);  \n[valter.santilli@uniroma1.it](valter.santilli@uniroma1.it) (V.S.)  \n2 Department of Computer Science Sapienza, University of Rome, 00198 Rome, Italy  \n3 San Salvatore Hospital, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Vetoio Stree, 67100 L'Aquila, Italy; [lucagiuliani92@virgilio.it](lucagiuliani92@virgilio.it)  \n4 Fisiocard Medical Centre, Via Francesco Tovaglieri 17, 00155 Rome, Italy; [carlo.ottonello@gmail.com](carlo.ottonello@gmail.com)  \n* [Correspondence: massimiliano.mangone@uniroma1.it](Correspondence: massimiliano.mangone@uniroma1.it)  \nAbstract: The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model's effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3","cbCaigHX2DjBV6z0","https://ap.wps.com/l/cbCaigHX2DjBV6z0","pdf",1440356,1,11,"English","en",105,"# Introduction\n## Knee injuries and clinical importance\n## MRI-based evaluation challenges\n# Methods\n## Real-life imaging protocol\n## Model architecture and targets\n# Results\n## Performance for meniscus tears\n## Performance for bone marrow edema\n## Performance for general abnormalities\n# Conclusions","[{\"question\":\"Why is automated analysis of knee MRI valuable for radiologists?\",\"answer\":\"MRI provides high detail that is difficult to interpret and can be time-consuming, especially when many scans must be reviewed quickly.\"},{\"question\":\"What knee conditions does the proposed model detect?\",\"answer\":\"The model detects medial meniscus tears, bone marrow edema, and general abnormalities in knee MRI exams.\"},{\"question\":\"How is the model performance evaluated?\",\"answer\":\"Effectiveness is measured using accuracy, sensitivity, and specificity for each targeted condition.\"}]","A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging | PDF",1785895556,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-for-knee-injury-detection-from-magnetic-resonance-imaging","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-for-knee-injury-detection-from-magnetic-resonance-imaging/124950/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is automated analysis of knee MRI valuable for radiologists?","Question",{"text":75,"@type":76},"MRI provides high detail that is difficult to interpret and can be time-consuming, especially when many scans must be reviewed quickly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What knee conditions does the proposed model detect?",{"text":80,"@type":76},"The model detects medial meniscus tears, bone marrow edema, and general abnormalities in knee MRI exams.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance evaluated?",{"text":84,"@type":76},"Effectiveness is measured using accuracy, sensitivity, and specificity for each targeted condition.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]