{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-assisted-diagnosis-for-knee","title":"Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet","arxiv_id":null,"date":"2018-11-27","proceeding":"Medicine 2018 11","authors":["Nicholas Bien","Pranav Rajpurkar","Robyn L. Ball","Jeremy Irvin","Allison Park","Erik Jones","Michael Bereket","Bhavik N. Patel","Kristen W. Yeom","Katie Shpanskaya","Safwan Halabi","Evan Zucker","Gary Fanton","Derek F. Amanatullah","Christopher F. Beaulieu","Geoffrey M. Riley","Russell J. Stewart","Francis G. Blankenberg","David B. Larson","Ricky H. Jones","Curtis P. Langlotz","Andrew Y. Ng","Matthew P. Lungren"],"abstract":"Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee\r\ninjuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic\r\nerror and variability. An automated system for interpreting knee MRI could prioritize highrisk patients and assist clinicians in making diagnoses. Deep learning methods, in being\r\nable to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a\r\ndeep learning model for detecting general abnormalities and specific diagnoses (anterior\r\ncruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured\r\nthe effect of providing the model’s predictions to clinical experts during interpretation.","url_abs":"https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002699","url_pdf":"https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1002699&type=printable","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-assisted-diagnosis-for-knee","repo_url":"https://github.com/ahmedbesbes/mrnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[{"slug":"mrnet","name":"MRNet","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-label-classification-on-mrnet","task":"Multi-Label Classification","dataset":"MRNet","model":"MRNet","rank_in_archive_order":1,"of":2,"metrics":{"AUC on ACL Tear (ACL)":"0.915","AUC on Abnormality (ABN)":"0.944","AUC on Meniscus Tear (MEN)":"0.822","Accuracy on ACL Tear (ACL)":"0.867","Accuracy on Abnormality (ABN)":"0.850","Accuracy on Meniscus Tear (MEN)":"0.725","Average AUC":"0.894","Average Accuracy":"0.814"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}