{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T20:41:09Z","timestamp":1776199269816,"version":"3.50.1"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":4,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004032","name":"Jilin University","doi-asserted-by":"publisher","award":["2024JBGS04"],"award-info":[{"award-number":["2024JBGS04"]}],"id":[{"id":"10.13039\/501100004032","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004032","name":"Jilin University","doi-asserted-by":"publisher","award":["2025B37"],"award-info":[{"award-number":["2025B37"]}],"id":[{"id":"10.13039\/501100004032","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472620"],"award-info":[{"award-number":["82472620"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20390"],"award-info":[{"award-number":["U21A20390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010211","name":"Education Department of Jilin Province","doi-asserted-by":"publisher","award":["JJKH20250176KJ"],"award-info":[{"award-number":["JJKH20250176KJ"]}],"id":[{"id":"10.13039\/501100010211","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015800","name":"Jilin Province Development and Reform Commission","doi-asserted-by":"publisher","award":["2023C039-3"],"award-info":[{"award-number":["2023C039-3"]}],"id":[{"id":"10.13039\/100015800","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Advanced Research"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.jare.2026.04.013","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T06:16:29Z","timestamp":1775456189000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multicenter deep learning for multi-abnormality screening on hip radiographs: development, external validation, and assisted reader study"],"prefix":"10.1016","author":[{"given":"Shenghao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaohui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qibo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianlin","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihuai","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7175-2726","authenticated-orcid":false,"given":"Jianlin","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongfeng","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanguo","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.jare.2026.04.013_b0005","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.2106\/JBJS.19.00346","article-title":"Hip joint capsular anatomy, mechanics, and surgical management","volume":"101","author":"Ng","year":"2019","journal-title":"J Bone Joint Surg Am"},{"issue":"Suppl 2","key":"10.1016\/j.jare.2026.04.013_b0010","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2106\/JBJS.J.01734","article-title":"Radiographic structural abnormalities associated with premature, natural hip-joint failure","volume":"93","author":"Clohisy","year":"2011","journal-title":"J Bone Joint Surg Am"},{"key":"10.1016\/j.jare.2026.04.013_b0015","doi-asserted-by":"crossref","unstructured":"Manaster BJ. From the RSNA refresher courses. Radiological society of north America. Adult chronic hip pain: Radiographic evaluation. Radiogr Rev Publ Radiol Soc N Am Inc 2000;20 Spec No:S3\u201325. 10.1148\/radiographics.20.suppl_1.g00oc06s3.","DOI":"10.1148\/radiographics.20.suppl_1.g00oc06s3"},{"key":"10.1016\/j.jare.2026.04.013_b0020","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5371\/hp.2024.36.1.47","article-title":"Prevalence of venous thromboembolism after immediate screening in hip fracture patients","volume":"36","author":"Kim","year":"2024","journal-title":"Hip Pelvis"},{"key":"10.1016\/j.jare.2026.04.013_b0025","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.2106\/JBJS.I.00575","article-title":"The natural history of untreated asymptomatic osteonecrosis of the femoral head: a systematic literature review","volume":"92","author":"Mont","year":"2010","journal-title":"J Bone Joint Surg Am"},{"key":"10.1016\/j.jare.2026.04.013_b0030","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1136\/annrheumdis-2020-218625","article-title":"10-year natural course of early hip osteoarthritis in middle-aged persons with hip pain: a CHECK study","volume":"80","author":"van Berkel","year":"2021","journal-title":"Ann Rheum Dis"},{"key":"10.1016\/j.jare.2026.04.013_b0035","article-title":"Cause of death and factors associated with early in-hospital mortality after hip fracture","volume":"97-B:246\u201351","author":"Chatterton","year":"2015","journal-title":"Bone Jt J"},{"key":"10.1016\/j.jare.2026.04.013_b0040","doi-asserted-by":"crossref","first-page":"e1027","DOI":"10.5435\/JAAOS-D-24-00420","article-title":"American academy of orthopaedic surgeons clinical practice guideline summary management of osteoarthritis of the hip","volume":"32","author":"Hannon","year":"2024","journal-title":"J Am Acad Orthop Surg"},{"key":"10.1016\/j.jare.2026.04.013_b0045","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.rcl.2022.03.005","article-title":"Imaging of osteoarthritis of the hip","volume":"60","author":"Walsh","year":"2022","journal-title":"Radiol Clin North Am"},{"key":"10.1016\/j.jare.2026.04.013_b0050","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1148\/radiol.210937","article-title":"Improving radiographic fracture recognition performance and efficiency using artificial intelligence","volume":"302","author":"Guermazi","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0055","doi-asserted-by":"crossref","DOI":"10.3389\/fradi.2023.1242902","article-title":"The promise and limitations of artificial intelligence in musculoskeletal imaging","volume":"3","author":"Debs","year":"2023","journal-title":"Front Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jare.2026.04.013_b0065","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"J Am Med Assoc"},{"key":"10.1016\/j.jare.2026.04.013_b0070","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1007\/s00330-019-06564-3","article-title":"Deep learning: definition and perspectives for thoracic imaging","volume":"30","author":"Chassagnon","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0075","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1007\/s11547-022-01476-7","article-title":"Automatic detection and classification of knee osteoarthritis using deep learning approach","volume":"127","author":"Abdullah","year":"2022","journal-title":"Radiol Med (Torino)"},{"key":"10.1016\/j.jare.2026.04.013_b0080","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/jbmr.4814","article-title":"Deep-learning-based detection of vertebral fracture and osteoporosis using lateral spine X-ray radiography","volume":"38","author":"Hong","year":"2023","journal-title":"J Bone Miner Res Off J Am Soc Bone Miner Res"},{"key":"10.1016\/j.jare.2026.04.013_b0085","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.arth.2022.10.003","article-title":"Deep learning approach for diagnosing early osteonecrosis of the femoral head based on magnetic resonance imaging","volume":"38","author":"Shen","year":"2023","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.jare.2026.04.013_b0090","first-page":"618","article-title":"Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"10.1016\/j.jare.2026.04.013_b0095","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1186\/s13018-025-06057-8","article-title":"From variability to consistency: building a kellgren-lawrence gonarthrosis dataset","volume":"20","author":"Beyaz","year":"2025","journal-title":"J Orthop Surg"},{"key":"10.1016\/j.jare.2026.04.013_b0100","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/ksa.70191","article-title":"Deep contrastive learning improves identification of early-stage knee osteoarthritis across multicohort X-ray datasets","volume":"34","author":"Jia","year":"2026","journal-title":"Knee Surg Sports Traumatol Arthrosc Off J ESSKA"},{"key":"10.1016\/j.jare.2026.04.013_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2025.112113","article-title":"AI for fracture diagnosis in clinical practice: four approaches to systematic AI-implementation and their impact on AI-effectiveness","volume":"187","author":"Loeffen","year":"2025","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2024.111375","article-title":"Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography","volume":"173","author":"Ruitenbeek","year":"2024","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0115","doi-asserted-by":"crossref","first-page":"E88","DOI":"10.1148\/radiol.2020202944","article-title":"Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: value of artificial intelligence","volume":"298","author":"Zhang","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0120","doi-asserted-by":"crossref","first-page":"E167","DOI":"10.1148\/radiol.2020203511","article-title":"DeepCOVID-XR: an artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large U.S. clinical data set","volume":"299","author":"Wehbe","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0125","doi-asserted-by":"crossref","first-page":"23895","DOI":"10.1038\/s41598-021-03265-0","article-title":"Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis","volume":"11","author":"Codlin","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.jare.2026.04.013_b0130","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1093\/cid\/ciy967","article-title":"Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs","volume":"69","author":"Hwang","year":"2019","journal-title":"Clin Infect Dis Off Publ Infect Dis Soc Am"},{"key":"10.1016\/j.jare.2026.04.013_b0135","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1148\/radiol.212213","article-title":"Deep learning detection of active pulmonary tuberculosis at chest radiography matched the clinical performance of radiologists","volume":"306","author":"Kazemzadeh","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0140","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1186\/s12891-024-07942-9","article-title":"Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification","volume":"25","author":"Rani","year":"2024","journal-title":"BMC Musculoskelet Disord"},{"key":"10.1016\/j.jare.2026.04.013_b0145","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2024.1437188","article-title":"OA-MEN: a fusion deep learning approach for enhanced accuracy in knee osteoarthritis detection and classification using X-ray imaging","volume":"12","author":"Ren","year":"2025","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.jare.2026.04.013_b0150","first-page":"1900","article-title":"Deep learning-based joint effusion classification in adult knee radiographs: a multi-center prospective study","volume":"14","author":"Won","year":"2024","journal-title":"Diagn Basel Switz"},{"key":"10.1016\/j.jare.2026.04.013_b0155","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1016\/j.acra.2023.10.036","article-title":"Automated segmentation and classification of knee synovitis based on MRI using deep learning","volume":"31","author":"Wang","year":"2024","journal-title":"Acad Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0160","first-page":"1263","article-title":"Deep learning-assisted automatic diagnosis of anterior cruciate ligament tear in knee magnetic resonance images","volume":"10","author":"Wang","year":"2024","journal-title":"Tomogr Ann Arbor Mich"},{"key":"10.1016\/j.jare.2026.04.013_b0165","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1002\/jbmr.4879","article-title":"Using artificial intelligence to diagnose osteoporotic vertebral fractures on plain radiographs","volume":"38","author":"Shen","year":"2023","journal-title":"J Bone Miner Res Off J Am Soc Bone Miner Res"},{"key":"10.1016\/j.jare.2026.04.013_b0170","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230614","article-title":"A CT-based deep learning model for predicting subsequent fracture risk in patients with hip fracture","volume":"310","author":"Kim","year":"2024","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0175","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00330-022-08950-w","article-title":"Lower-extremity fatigue fracture detection and grading based on deep learning models of radiographs","volume":"33","author":"Wang","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0180","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.knee.2025.02.001","article-title":"Deep learning for tibial plateau fracture detection and classification","volume":"54","author":"van der Gaast","year":"2025","journal-title":"Knee"},{"key":"10.1016\/j.jare.2026.04.013_b0185","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2023.1146910","article-title":"Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs","volume":"14","author":"Ye","year":"2023","journal-title":"Front Physiol"},{"key":"10.1016\/j.jare.2026.04.013_b0190","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.30617","article-title":"Deep learning model to classify and monitor idiopathic scoliosis in adolescents using a single smartphone photograph","volume":"6","author":"Zhang","year":"2023","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.jare.2026.04.013_b0195","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s41747-024-00470-0","article-title":"Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study","volume":"8","author":"Tang","year":"2024","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.jare.2026.04.013_b0200","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1007\/s00330-023-10308-9","article-title":"Deep learning-based identification of spine growth potential on EOS radiographs","volume":"34","author":"Xie","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jare.2026.04.013_b0205","first-page":"2658","article-title":"An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images","volume":"13","author":"Saeed","year":"2023","journal-title":"Diagn Basel Switz"},{"key":"10.1016\/j.jare.2026.04.013_b0210","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1148\/radiol.2017171920","article-title":"Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction","volume":"286","author":"Park","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.jare.2026.04.013_b0215","doi-asserted-by":"crossref","first-page":"7637","DOI":"10.1038\/s41467-024-51888-4","article-title":"Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities","volume":"15","author":"Qiu","year":"2024","journal-title":"Nat Commun"},{"issue":"25","key":"10.1016\/j.jare.2026.04.013_b0220","first-page":"00018","article-title":"Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions","volume":"S0168\u20138278","author":"Ding","year":"2025","journal-title":"J Hepatol"},{"key":"10.1016\/j.jare.2026.04.013_b0225","article-title":"Deep learning based screening model for hip diseases on plain radiographs","volume":"20","author":"Park","year":"2025","journal-title":"PLoS One"}],"container-title":["Journal of Advanced Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2090123226002924?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2090123226002924?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T19:34:41Z","timestamp":1776195281000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2090123226002924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":45,"alternative-id":["S2090123226002924"],"URL":"https:\/\/doi.org\/10.1016\/j.jare.2026.04.013","relation":{},"ISSN":["2090-1232"],"issn-type":[{"value":"2090-1232","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multicenter deep learning for multi-abnormality screening on hip radiographs: development, external validation, and assisted reader study","name":"articletitle","label":"Article Title"},{"value":"Journal of Advanced Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jare.2026.04.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V. on behalf of Cairo University.","name":"copyright","label":"Copyright"}]}}