{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:13:49Z","timestamp":1788966829616,"version":"build-2803163510"},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["arthroplastyjournal.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Arthroplasty"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.arth.2026.07.013","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T16:27:20Z","timestamp":1786033640000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of Artificial Intelligence Systems in Early Prediction of Dislocation and Aseptic Loosening After Total Hip Arthroplasty"],"prefix":"10.1016","author":[{"given":"Mehmet S.","family":"Abul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4883-5579","authenticated-orcid":false,"given":"\u00d6mer F.","family":"Sevim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naz\u0131m C.","family":"K\u0131l\u0131\u00e7","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzaffer","family":"Agir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Tuncay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.arth.2026.07.013_bib1","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1016\/j.arth.2021.02.026","article-title":"A deep learning tool for automated radiographic measurement of acetabular component inclination and version After total hip arthroplasty","volume":"36","author":"Rouzrokh","year":"2021","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.arth.2026.07.013_bib2","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1007\/s43465-020-00217-0","article-title":"Total hip arthroplasty digital templating: size predicting ability and interobserver variability","volume":"54","author":"Montiel","year":"2020","journal-title":"Indian J Orthop"},{"key":"10.1016\/j.arth.2026.07.013_bib3","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1302\/0301-620X.104B8.BJJ-2022-0120.R2","article-title":"Artificial intelligence for image analysis in total hip and total knee arthroplasty: a scoping review","volume":"104-B","author":"Gurung","year":"2022","journal-title":"Bone Joint J"},{"key":"10.1016\/j.arth.2026.07.013_bib4","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1111\/os.70156","article-title":"Comparison of artificial intelligence and traditional methods in preoperative planning for primary total hip arthroplasty: a systematic review and meta analysis","volume":"17","author":"Xue","year":"2025","journal-title":"Orthop Surg"},{"key":"10.1016\/j.arth.2026.07.013_bib5","article-title":"Advantages and effectiveness of AI three-dimensional reconstruction technology in the preoperative planning of total hip arthroplasty","volume":"15","author":"Li","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.arth.2026.07.013_bib6","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2024.1417729","article-title":"The role of artificial intelligence in preoperative planning for total hip arthroplasty: a systematic review","volume":"7","author":"Mozafari","year":"2024","journal-title":"Front Artif Intell"},{"key":"10.1016\/j.arth.2026.07.013_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.otsr.2025.104543","article-title":"Artificial intelligence in hip and knee surgery: a bibliometric analysis of the 50 most cited articles","author":"De Geofroy","year":"2025","journal-title":"Orthop Traumatol Surg Res"},{"key":"10.1016\/j.arth.2026.07.013_bib8","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1007\/s00264-022-05644-2","article-title":"Hip arthroplasty dislocation risk calculator: evaluation of one million primary implants and twenty-five thousand dislocations with deep learning artificial intelligence in a systematic review of reviews","volume":"47","author":"Hernigou","year":"2023","journal-title":"Int Orthop"},{"key":"10.1016\/j.arth.2026.07.013_bib9","doi-asserted-by":"crossref","DOI":"10.1002\/jeo2.70195","article-title":"Revolutionizing total hip arthroplasty: the role of artificial intelligence and machine learning","volume":"12","author":"Longo","year":"2025","journal-title":"J Exp Orthop"},{"key":"10.1016\/j.arth.2026.07.013_bib10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s12893-024-02752-1","article-title":"Efficacy of an artificial intelligence preoperative planning system for assisting in revision surgery after artificial total hip arthroplasty","volume":"25","author":"Zhu","year":"2025","journal-title":"BMC Surg"},{"key":"10.1016\/j.arth.2026.07.013_bib11","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00256-024-04692-6","article-title":"Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip","volume":"54","author":"Tan","year":"2025","journal-title":"Skeletal Radiol"},{"key":"10.1016\/j.arth.2026.07.013_bib12","first-page":"609","article-title":"Intra-op measurement of the mechanical axis deviation: an evaluation study on 19 human cadaver legs","volume":"15","author":"Wang","year":"2012","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"10.1016\/j.arth.2026.07.013_bib13","doi-asserted-by":"crossref","DOI":"10.3389\/fsurg.2022.1022937","article-title":"Clinical efficacy of direct anterior approach vs. other surgical approaches for total hip arthroplasty: a systematic review and meta-analysis based on RCTs","volume":"9","author":"Zhou","year":"2022","journal-title":"Front Surg"},{"key":"10.1016\/j.arth.2026.07.013_bib14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/jpm15010021","article-title":"The role of artificial intelligence and emerging technologies in advancing total hip arthroplasty","volume":"15","author":"Andriollo","year":"2025","journal-title":"J Pers Med"},{"key":"10.1016\/j.arth.2026.07.013_bib15","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1016\/j.arth.2019.05.055","article-title":"Artificial intelligence and machine learning in lower extremity arthroplasty: a review","volume":"34","author":"Haeberle","year":"2019","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.arth.2026.07.013_bib16","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.2106\/JBJS.21.01229","article-title":"Applying deep learning to establish a total hip arthroplasty radiography registry: a stepwise approach","volume":"104","author":"Rouzrokh","year":"2022","journal-title":"J Bone Joint Surg Am"},{"key":"10.1016\/j.arth.2026.07.013_bib17","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1016\/j.arth.2022.10.039","article-title":"The impact of machine learning on total joint arthroplasty patient outcomes: a systemic review","volume":"38","author":"Karlin","year":"2023","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.arth.2026.07.013_bib18","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1007\/s00264-023-05856-0","article-title":"Revision hip arthroplasty dislocation risk calculator: when to select dual mobility, large heads, constrained liners, or a standard head size? Testing one hundred thousand hip revisions with artificial intelligence","volume":"47","author":"Hernigou","year":"2023","journal-title":"Int Orthop"},{"key":"10.1016\/j.arth.2026.07.013_bib19","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.220067","article-title":"Patient-specific hip arthroplasty dislocation risk calculator: an explainable multimodal machine Learning\u2013based approach","volume":"4","author":"Khosravi","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.arth.2026.07.013_bib20","doi-asserted-by":"crossref","first-page":"782","DOI":"10.3390\/medicina59040782","article-title":"Detection of prosthetic loosening in hip and knee arthroplasty using machine learning: a systematic review and meta-analysis","volume":"59","author":"Kim","year":"2023","journal-title":"Medicina (Mex)"},{"key":"10.1016\/j.arth.2026.07.013_bib21","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1016\/j.arth.2021.02.028","article-title":"Deep learning artificial intelligence model for assessment of hip dislocation risk following primary total hip arthroplasty from postoperative radiographs","volume":"36","author":"Rouzrokh","year":"2021","journal-title":"J Arthroplasty"},{"key":"10.1016\/j.arth.2026.07.013_bib22","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1302\/2633-1462.31.BJO-2021-0123.R1","article-title":"Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research: a call to emphasize data quality and indications","volume":"3","author":"Kunze","year":"2022","journal-title":"Bone Jt Open"},{"key":"10.1016\/j.arth.2026.07.013_bib23","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s11517-023-02779-1","article-title":"Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis\u2014Review of literature and in vitro case study","volume":"61","author":"Ampadi Ramachandran","year":"2023","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.arth.2026.07.013_bib24","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.3390\/healthcare11111524","article-title":"Challenges of integrating new technologies for orthopedic doctors to face up to difficulties during the pandemic era","volume":"11","author":"Niculescu","year":"2023","journal-title":"Healthcare"}],"container-title":["The Journal of Arthroplasty"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0883540326007618?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0883540326007618?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:25:05Z","timestamp":1788963905000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0883540326007618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":24,"alternative-id":["S0883540326007618"],"URL":"https:\/\/doi.org\/10.1016\/j.arth.2026.07.013","relation":{},"ISSN":["0883-5403"],"issn-type":[{"value":"0883-5403","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Diagnostic Accuracy of Artificial Intelligence Systems in Early Prediction of Dislocation and Aseptic Loosening After Total Hip Arthroplasty","name":"articletitle","label":"Article Title"},{"value":"The Journal of Arthroplasty","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.arth.2026.07.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}