{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T14:55:53Z","timestamp":1787928953087,"version":"build-2784847793"},"reference-count":27,"publisher":"The Ewha Medical Journal, Ewha Womans University College of Medicine","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":["www.e-emj.org"],"crossmark-restriction":true},"short-container-title":["Ewha Med J"],"abstract":"<jats:p>\n                Shoulder diseases pose a significant health challenge for older adults, often\n                    causing pain, functional decline, and decreased independence. This narrative\n                    review explores how deep learning (DL) can address diagnostic challenges by\n                    automating tasks such as image segmentation, disease detection, and motion\n                    analysis. Recent research highlights the effectiveness of DL-based convolutional\n                    neural networks and machine learning frameworks in diagnosing various shoulder\n                    pathologies. Automated image analysis facilitates the accurate assessment of\n                    rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT\n                    scans, frequently matching or surpassing the accuracy of human experts.\n                    Convolutional neural network-based systems are also adept at classifying\n                    fractures and joint conditions, enabling the rapid identification of common\n                    causes of shoulder pain from plain radiographs. Furthermore, advanced techniques\n                    like pose estimation provide precise measurements of the shoulder joint's\n                    range of motion and support personalized rehabilitation plans. These automated\n                    approaches have also been successful in quantifying local osteoporosis,\n                    utilizing machine learning-derived indices to classify bone density status. DL\n                    has demonstrated significant potential to improve diagnostic accuracy,\n                    efficiency, and consistency in the management of shoulder diseases in older\n                    patients. Machine learning-based assessments of imaging data and motion\n                    parameters can help clinicians optimize treatment plans and improve patient\n                    outcomes. However, to ensure their generalizability, reproducibility, and\n                    effective integration into routine clinical workflows, large-scale, prospective\n                    validation studies are necessary. As data availability and computational\n                    resources increase, the ongoing development of DL-driven applications is\n                    expected to further advance and personalize musculoskeletal care, benefiting\n                    both healthcare providers and the aging population.\n            <\/jats:p>","DOI":"10.12771\/emj.2025.e6","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T09:20:41Z","timestamp":1740388841000},"update-policy":"https:\/\/doi.org\/10.12771\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Application of deep learning for diagnosis of shoulder diseases in\n                    older adults: a narrative review"],"prefix":"10.12771","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7087-4219","authenticated-orcid":false,"given":"Sung Min","family":"Rhee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4998","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"issue":"3","key":"key2.0250224102027e+13_B1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.5397\/cise.2023.00430","article-title":"Rotator cuff degeneration and healing after rotator cuff\n                        repair","volume":"26","author":"Gumina","year":"2023","journal-title":"Clin Shoulder Elb"},{"issue":"1","key":"key2.0250224102027e+13_B2","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1186\/s12891-022-05973-8","article-title":"A systematic review of the global prevalence and incidence of\n                        shoulder pain","volume":"23","author":"Lucas","year":"2022","journal-title":"BMC Musculoskelet Disord"},{"issue":"1","key":"key2.0250224102027e+13_B3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s40945-021-00119-w","article-title":"Shoulder pain prevalence by age and within occupational groups: a\n                        systematic review","volume":"11","author":"Hodgetts","year":"2021","journal-title":"Arch Physiother"},{"issue":"4","key":"key2.0250224102027e+13_B4","doi-asserted-by":"crossref","first-page":"390","DOI":"10.5397\/cise.2023.00346","article-title":"Glenohumeral versus subacromial steroid injections for\n                        impingement syndrome with mild stiffness: a randomized controlled\n                        trial","volume":"26","author":"Kim","year":"2023","journal-title":"Clin Shoulder Elb"},{"issue":"3","key":"key2.0250224102027e+13_B5","doi-asserted-by":"crossref","first-page":"172","DOI":"10.5397\/cise.2021.00311","article-title":"Arthroscopic release in adhesive capsulitis of the shoulder: a\n                        retrospective study with 2 to 6 years of follow-up","volume":"24","author":"Mardani-Kivi","year":"2021","journal-title":"Clin Shoulder Elb"},{"issue":"3","key":"key2.0250224102027e+13_B6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.5397\/cise.2024.00262","article-title":"Does preoperative forward elevation weakness affect clinical\n                        outcomes in anatomic or reverse total shoulder arthroplasty patients with\n                        glenohumeral osteoarthritis and intact rotator cuff?","volume":"27","author":"Hones","year":"2024","journal-title":"Clin Shoulder Elb"},{"issue":"9","key":"key2.0250224102027e+13_B7","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1007\/s00167-023-07454-4","article-title":"Rotator cuff repairs with all-suture tape anchors: no difference\n                        in outcomes between with or without all-suture tape anchors","volume":"31","author":"Rhee","year":"2023","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"issue":"8","key":"key2.0250224102027e+13_B8","doi-asserted-by":"crossref","first-page":"E482","DOI":"10.1016\/j.jse.2020.11.007","article-title":"Effects of comorbidities on the outcomes of manipulation under\n                        anesthesia for primary stiff shoulder","volume":"30","author":"Ko","year":"2021","journal-title":"J Shoulder Elbow Surg"},{"issue":"5","key":"key2.0250224102027e+13_B9","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s00402-018-3095-6","article-title":"Accuracy of magnetic resonance imaging (MRI) for subscapularis\n                        tear: a systematic review and meta-analysis of diagnostic\n                        studies","volume":"139","author":"Malavolta","year":"2019","journal-title":"Arch Orthop Trauma Surg"},{"issue":"2","key":"key2.0250224102027e+13_B10","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4103\/1110-6611.196778","article-title":"Inter-rater and intra-rater reliability of Kinovea software for\n                        measurement of shoulder range of motion","volume":"21","author":"Elrahim","year":"2016","journal-title":"Bull Fac Phys Ther"},{"issue":"2","key":"key2.0250224102027e+13_B11","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s00256-021-03740-9","article-title":"Deep learning for accurately recognizing common causes of\n                        shoulder pain on radiographs","volume":"51","author":"Grauhan","year":"2022","journal-title":"Skeletal Radiol"},{"issue":"1","key":"key2.0250224102027e+13_B12","doi-asserted-by":"crossref","first-page":"15065","DOI":"10.1038\/s41598-021-93026-w","article-title":"Deep-learning framework and computer assisted fatty infiltration\n                        analysis for the supraspinatus muscle in MRI","volume":"11","author":"Ro","year":"2021","journal-title":"Sci Rep"},{"issue":"3","key":"key2.0250224102027e+13_B13","first-page":"297","article-title":"Deep learning model for measurement of shoulder critical angle\n                        and acromion index on shoulder radiographs","volume":"2","author":"Shariatnia","year":"2022","journal-title":"JSES Rev Rep Tech"},{"issue":"1","key":"key2.0250224102027e+13_B14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s00330-020-07070-7","article-title":"Deep learning for the rapid automatic quantification and\n                        characterization of rotator cuff muscle degeneration from shoulder CT\n                        datasets","volume":"31","author":"Taghizadeh","year":"2021","journal-title":"Eur Radiol"},{"key":"key2.0250224102027e+13_B15","doi-asserted-by":"crossref","first-page":"102935","DOI":"10.1016\/j.artmed.2024.102935","article-title":"Comprehensive review of deep learning in orthopaedics:\n                        applications, challenges, trustworthiness, and fusion","volume":"155","author":"Alzubaidi","year":"2024","journal-title":"Artif Intell 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analysis\n                        using machine learning techniques: An innovative approach in medical\n                        research","volume":"10","author":"Li","year":"2024","journal-title":"Heliyon"},{"issue":"4","key":"key2.0250224102027e+13_B22","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1302\/0301-620X.85B4.12843","article-title":"The cortical thickness of the proximal humeral diaphysis predicts\n                        bone mineral density of the proximal humerus","volume":"85","author":"Tingart","year":"2003","journal-title":"J Bone Joint Surg Br"},{"issue":"2","key":"key2.0250224102027e+13_B23","doi-asserted-by":"crossref","first-page":"534","DOI":"10.3390\/s24020534","article-title":"Comparison of shoulder range of motion quantified with mobile\n                        phone video-based skeletal tracking and 3D motion capture: preliminary\n                        study","volume":"24","author":"van den Hoorn","year":"2024","journal-title":"Sensors"},{"issue":"1","key":"key2.0250224102027e+13_B24","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3390\/s24010158","article-title":"A machine learning app for monitoring physical therapy at\n                        home","volume":"24","author":"Pereira","year":"2024","journal-title":"Sensors"},{"key":"key2.0250224102027e+13_B25","first-page":"5693","volume-title":"Deep high-tesolution representation learning for human pose estimation.\n                        Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern\n                        Recognition (CVPR)","author":"Sun","year":"2019"},{"issue":"9","key":"key2.0250224102027e+13_B26","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3390\/s24092912","article-title":"Estimation of shoulder joint rotation angle using tablet device\n                        and pose estimation artificial intelligence 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