{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T14:50:54Z","timestamp":1791384654984,"version":"4.3.3"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Oral Implants Res"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Peri\u2010implantitis is a common implant complication requiring early detection to prevent bone loss and implant failure. Deep learning models show promise for enhancing radiographic diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This review systematically evaluated the diagnostic performance of deep learning models in detecting peri\u2010implant marginal bone loss on radiographic images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      A comprehensive search of PubMed, EMBASE, CENTRAL,\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/clinicaltrials.gov\">ClinicalTrials.gov<\/jats:ext-link>\n                      , and ProQuest identified studies published between 2010 and July 2025. Two reviewers independently screened studies, extracted data, and assessed methodological quality using QUADAS\u20102. Diagnostic metrics, including sensitivity, specificity, F1\u2010score, area under the curve (AUC), were synthesized using random\u2010effects meta\u2010analysis. Heterogeneity and publication bias were evaluated using\n                      <jats:italic>I<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      statistics, meta\u2010regression, and funnel plots.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five studies comprising 12,545 periapical and panoramic radiographs met inclusion criteria. Deep learning models achieved pooled sensitivity of 88%, specificity of 91%, and AUC of 0.95, indicating high diagnostic performance. Positive and negative likelihood ratios suggested strong clinical utility. Quality was generally good, though reporting of implant characteristics and data augmentation was inconsistent. Meta\u2010regression revealed that dataset size and unit of analysis influenced accuracy, whereas imaging type did not. No publication bias was found.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning models demonstrate high accuracy in detecting radiographic marginal bone loss, potentially indicating peri\u2010implantitis but cannot substitute for comprehensive clinical assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>These models offer a promising adjunct for radiographic detection of marginal bone loss, supporting clinicians in early diagnosis and timely interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/clr.70090","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:09:28Z","timestamp":1769004568000},"page":"385-401","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Diagnostic Accuracy of Deep Learning Models in Detecting Peri\u2010Implant Marginal Bone Loss: A Systematic Review and Meta\u2010Analysis"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4019-9491","authenticated-orcid":false,"given":"Momen A.","family":"Atieh","sequence":"first","affiliation":[{"name":"Hamdan Bin Mohammed College of Dental Medicine Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City  Dubai UAE"},{"name":"Sir John Walsh Research Institute, Faculty of Dentistry University of Otago  Dunedin New Zealand"},{"name":"School of Dentistry University of Jordan  Amman Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maanas","family":"Shah","sequence":"additional","affiliation":[{"name":"Hamdan Bin Mohammed College of Dental Medicine Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abeer","family":"Hakam","sequence":"additional","affiliation":[{"name":"Hamdan Bin Mohammed College of Dental Medicine Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fawaghi","family":"AlAli","sequence":"additional","affiliation":[{"name":"Hamdan Bin Mohammed College of Dental Medicine Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samhar","family":"AlSayed","sequence":"additional","affiliation":[{"name":"Hamdan Bin Mohammed College of Dental Medicine Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City  Dubai UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Tawse\u2010Smith","sequence":"additional","affiliation":[{"name":"Sir John Walsh Research Institute, Faculty of Dentistry University of Otago  Dunedin New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2003-3494","authenticated-orcid":false,"given":"Nabeel H. 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