{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T20:08:54Z","timestamp":1791317334330,"version":"4.3.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"European Union\u2019s Horizon 2020"},{"name":"Research and Innovation Program","award":["857287"],"award-info":[{"award-number":["857287"]}]},{"name":"University of North Carolina Biomedical Institutional Review Board","award":["#19-1430"],"award-info":[{"award-number":["#19-1430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>This study aimed to assess the impact of a deep learning model on oral radiologists\u2019 ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>This study used an annotated dataset and a beta version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for the presence\/absence of apical radiolucencies. Four oral radiologists participated in a cross-over reading scenario, analysing the radiographs under 2 conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using Alternative Free-Response Receiver Operating Characteristic - Area Under the Curve (AFROC-AUC), sensitivity, specificity, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (\u03b2\u2009=\u200912, 95% CI, 11-13, P\u2009&amp;lt;\u20090.001), while radiologists\u2019 professional status was positively associated with diagnostic accuracy (\u03b2\u2009=\u20090.02, 95% CI, 0.00-0.04, P\u2009=\u20090.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists\u2019 experience in diagnostic accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>AI did not significantly enhance radiologists\u2019 overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/dmfr\/twae054","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T18:37:57Z","timestamp":1733855877000},"page":"118-124","source":"Crossref","is-referenced-by-count":9,"title":["The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4081-7675","authenticated-orcid":false,"given":"Manal H","family":"Hamdan","sequence":"first","affiliation":[{"name":"Department of Surgical and Diagnostic Sciences, Marquette University School of Dentistry , Milwaukee, WI 53233,","place":["United 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