{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:56:15Z","timestamp":1791186975730,"version":"4.1.0"},"reference-count":75,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"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":["Health Science Reports"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Differentiating odontogenic keratocyst (OKC) from other radiolucent jaw lesions like ameloblastoma is clinically important but radiographically difficult. Recent advances in artificial intelligence (AI) show promise for enhancing diagnosis using cone\u2010beam computed tomography (CBCT). This study aims to systematically evaluate and meta\u2010analyze the diagnostic accuracy of AI models in detecting OKCs on CBCT imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A systematic review and meta\u2010analysis was conducted according to PRISMA\u2010DTA guidelines. Five electronic databases were searched through July 6, 2025. Studies employing AI models for OKC detection using CBCT were included. Methodological quality was assessed using QUADAS\u20102. Pooled estimates were computed using a random\u2010effects model, with heterogeneity evaluated via I\n                      <jats:sup>2<\/jats:sup>\n                      and meta\u2010regression. The Eager test and funnel plot were employed to assess publication bias.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Twelve studies were included. AI models demonstrated high diagnostic accuracy, characterized by a pooled sensitivity of 89% (95% CI: 79%\u201395%) and specificity of 92% (95% CI: 81%\u201397%), both exceeding 85%, along with a substantial diagnostic odds ratio (87.06) and a robust discriminative ability (AUC\u2009=\u20090.828). Deep learning (DL) models achieved higher sensitivity (91%) than machine learning (ML) models (86%), while ML models showed slightly higher specificity. Heterogeneity was substantial (I\n                      <jats:sup>2<\/jats:sup>\n                      = 78%\u201393%). Publication year explained 57.2% of the variability in sensitivity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010based models, particularly CNN\u2010based DL architectures, demonstrate clinically relevant diagnostic performance with high sensitivity, specificity, and diagnostic odds ratios, supporting their potential role as adjunctive tools in CBCT\u2010based differentiation of OKCs from other odontogenic lesions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.73086","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T11:29:47Z","timestamp":1787743787000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta\u2010Analysis"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1658-8983","authenticated-orcid":false,"given":"Reyhaneh","family":"Shoorgashti","sequence":"first","affiliation":[{"name":"Department of Oral Medicine, Tehran Medical Sciences Islamic Azad University (TeMS.C.) Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3877-328X","authenticated-orcid":false,"given":"Simin","family":"Lesan","sequence":"additional","affiliation":[{"name":"Department of Diagnosis and Oral Health University of Louisville School of Dentistry Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7564-570X","authenticated-orcid":false,"given":"Sarah Sadat","family":"Ehsani","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine, Tehran Medical Sciences Islamic Azad University (TeMS.C.) Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2309-6396","authenticated-orcid":false,"given":"Asma","family":"Yazdanfar","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine, Tehran Medical Sciences Islamic Azad University (TeMS.C.) 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