{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:58Z","timestamp":1785545038111,"version":"3.56.0"},"reference-count":30,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Supraspinatus tendon pathologies are common causes of shoulder pain. Magnetic resonance imaging (MRI) is the reference imaging method but requires expert interpretation. Automated classification may improve diagnostic consistency and support musculoskeletal imaging workflows.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to develop and evaluate a hierarchical deep learning model to classify supraspinatus tendon status as intact tendons, tendinopathy\/partial-thickness tears, or full-thickness tears.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A total of 1192 shoulder MRI scans were analyzed. The hierarchical system consisted of a left-right orientation classifier, a full-thickness tear detector (model F), and a classifier for distinguishing intact tendons from tendinopathy\/partial-thickness tears (model ITP). A flat 3-class model served as a baseline comparator. Performance was evaluated on both an internal test set and an independent external cohort.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n On the internal test set, the hierarchical system achieved a system-level sensitivity of 68.1% for tendinopathy\/partial-thickness tears, outperforming the flat baseline (57.4%) while maintaining comparable sensitivity for full-thickness tears (hierarchical vs flat: 94.1% vs 95.1%). On the independent external cohort, the sensitivity for tendinopathy\/partial-thickness tears was 45.5% for the hierarchical model and 18.2% for the flat baseline. The hierarchical model also showed a numerically higher balanced accuracy (hierarchical vs flat: 68.1% vs 64.5%), macro\n F<\/jats:italic>\n 1<\/jats:sub>\n -score, and macro area under the curve, although its overall accuracy was lower (76.4% vs 79.8%).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n A hierarchical deep learning approach that mirrors clinical diagnostic reasoning may improve the recognition of tendinopathy and partial-thickness tears, a challenging category for nonspecialist readers. Given the overlapping CIs, these findings should be interpreted as indicative of a trend rather than definitive improvement. External validation supports feasibility across different MRI sources, though the predominance of data from a single institution limits generalizability and warrants further prospective evaluation.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/84804","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T03:35:42Z","timestamp":1783568142000},"page":"e84804-e84804","source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted Detection of Supraspinatus Tendon Pathologies Using a Hierarchical Deep Learning Model to Improve Clinical Applicability: Development and Evaluation Study"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-8867","authenticated-orcid":false,"given":"Kun-Hui","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-5487","authenticated-orcid":false,"given":"Jacky Chung-Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1025-3437","authenticated-orcid":false,"given":"Hsin-Yu","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8691-8186","authenticated-orcid":false,"given":"En-Rung","family":"Chiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1833-5944","authenticated-orcid":false,"given":"Hsuan-Hsiao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8527-3320","authenticated-orcid":false,"given":"Hsin-Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4392-3361","authenticated-orcid":false,"given":"Henry Horng-Shing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9899-3159","authenticated-orcid":false,"given":"Chih-Yu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2023.857821","article-title":"Present situation and development prospects of the diagnosis and treatment of rotator cuff tears","volume":"10","author":"Zhou","journal-title":"Front Surg"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1302\/0301-620X.77B2.7706351","article-title":"Rotator-cuff changes in asymptomatic adults. The effect of age, hand dominance and gender","volume":"77","author":"Milgrom","journal-title":"J Bone Joint Surg Br"},{"issue":"9","key":"R3","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-059175","article-title":"Prevalence of rotator cuff tendon tears and symptoms in a Chingford general population cohort, and the resultant impact on UK health services: a cross-sectional observational study","volume":"12","author":"Hinsley","journal-title":"BMJ Open"},{"issue":"9","key":"R4","doi-asserted-by":"publisher","first-page":"627","DOI":"10.2340\/16501977-2598","article-title":"Risk factors for rotator cuff tendinopathy: a systematic review and meta-analysis","volume":"51","author":"Leong","journal-title":"J Rehabil Med"},{"issue":"6","key":"R5","doi-asserted-by":"publisher","first-page":"e261","DOI":"10.1016\/j.jse.2020.08.028","article-title":"The inflammatory response of the supraspinatus muscle in rotator cuff tear conditions","volume":"30","author":"Frich","journal-title":"J Shoulder Elbow Surg"},{"issue":"6","key":"R6","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.2214\/ajr.168.6.9168717","article-title":"MR imaging of rotator cuff tendon tears: comparison of T2*-weighted gradient-echo and conventional dual-echo sequences","volume":"168","author":"Parsa","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01607-w","article-title":"Rotator cuff tear patterns: MRI appearance and its surgical relevance","volume":"15","author":"Yubran","journal-title":"Insights Imaging"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","first-page":"79","DOI":"10.5397\/cise.2022.01417","article-title":"Partial-thickness rotator cuff tears: a review of current literature on evaluation and management","volume":"27","author":"Radhakrishnan","journal-title":"Clin Shoulder Elb"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.3389\/fradi.2023.1242902","article-title":"The promise and limitations of artificial intelligence in musculoskeletal imaging","volume":"3","author":"Debs","journal-title":"Front Radiol"},{"issue":"12","key":"R10","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"Pinto-Coelho","journal-title":"Bioengineering (Basel)"},{"issue":"11","key":"R11","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15111315","article-title":"Artificial intelligence in rotator cuff tear detection: a systematic review of MRI-based models","volume":"15","author":"Longo","journal-title":"Diagnostics (Basel)"},{"issue":"7","key":"R12","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e28731","article-title":"Artificial intelligence powered advancements in upper extremity joint MRI: a review","volume":"10","author":"Chen","journal-title":"Heliyon"},{"issue":"6","key":"R13","doi-asserted-by":"publisher","first-page":"3538","DOI":"10.1007\/s00330-023-10392-x","article-title":"MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries","volume":"34","author":"Ni","journal-title":"Eur Radiol"},{"issue":"4","key":"R14","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1136\/ard.16.4.494","article-title":"Radiological assessment of osteo-arthrosis","volume":"16","author":"Kellgren","journal-title":"Ann Rheum Dis"},{"issue":"10","key":"R15","doi-asserted-by":"publisher","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","journal-title":"Clin Orthop Relat Res"},{"issue":"9","key":"R16","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1007\/s00256-018-2923-7","article-title":"Radiological glenohumeral osteoarthritis in long-term type 1 diabetes. Prevalence and reliability of three classification systems. The Dialong shoulder study","volume":"47","author":"Juel","journal-title":"Skeletal Radiol"},{"issue":"3","key":"R17","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1177\/03635465231166077","article-title":"Reliability of magnetic resonance imaging criteria for the preoperative assessment of rotator cuff tears: a systematic review","volume":"52","author":"Adriani","journal-title":"Am J Sports Med"},{"issue":"8","key":"R18","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.2006.034421","article-title":"Reliability of MRI assessment of supraspinatus tendinopathy","volume":"41","author":"Sein","journal-title":"Br J Sports Med"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.21025","article-title":"Role of magnetic resonance imaging in the evaluation of rotator cuff tears","volume":"14","author":"Koganti","journal-title":"Cureus"},{"key":"R20","doi-asserted-by":"crossref","unstructured":"Tran D Wang H Torresani L Ray J LeCun Y Paluri M . A closer look at spatiotemporal convolutions for action recognition. Presented at: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Jun 18-23, 2018; Salt Lake City, UT, USA. [doi: 10.1109\/CVPR.2018.00675]","DOI":"10.1109\/CVPR.2018.00675"},{"issue":"249-59","key":"R21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","journal-title":"Neural Netw"},{"key":"R22","doi-asserted-by":"crossref","unstructured":"Wang H Wang Z Du M et al. Score-CAM: score-weighted visual explanations for convolutional neural networks. Presented at: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Jun 14-19, 2020; Seattle, WA, USA. [doi: 10.1109\/CVPRW50498.2020.00020]","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"R23","doi-asserted-by":"crossref","unstructured":"Selvaraju RR Cogswell M Das A Vedantam R Parikh D Batra D . Grad-CAM: visual explanations from deep networks via gradient-based localization. Presented at: 2017 IEEE International Conference on Computer Vision (ICCV); Oct 22-29, 2017; Venice, Italy. [doi: 10.1109\/ICCV.2017.74]","DOI":"10.1109\/ICCV.2017.74"},{"issue":"6","key":"R24","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1097\/RLI.0000000000000951","article-title":"Deep learning diagnosis and classification of rotator cuff tears on shoulder MRI","volume":"58","author":"Lin","journal-title":"Invest Radiol"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-023-03909-z","article-title":"Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears","volume":"18","author":"Guo","journal-title":"J Orthop Surg Res"},{"issue":"20","key":"R26","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13203254","article-title":"Deep-learning-based automated rotator cuff tear screening in three planes of shoulder MRI","volume":"13","author":"Lee","journal-title":"Diagnostics (Basel)"},{"issue":"5","key":"R27","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e15804","article-title":"Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI)","volume":"9","author":"Esfandiari","journal-title":"Heliyon"},{"issue":"9","key":"R28","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1007\/s00256-022-04008-6","article-title":"Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI","volume":"51","author":"Yao","journal-title":"Skeletal Radiol"},{"key":"R29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asmart.2025.04.005","article-title":"Detection and classification of supraspinatus pathologies on shoulder magnetic resonance images using a code-free deep learning application","volume":"42","author":"Engin","journal-title":"Asia Pac J Sports Med Arthrosc Rehabil Technol"},{"issue":"2","key":"R30","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220062","article-title":"Performance and usability of code-free deep learning for chest radiograph classification, object detection, and segmentation","volume":"5","author":"Santomartino","journal-title":"Radiol Artif Intell"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e84804","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:25:56Z","timestamp":1785536756000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e84804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,8]]},"references-count":30,"URL":"https:\/\/doi.org\/10.2196\/84804","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,8]]},"article-number":"v14i2e84804"}}