{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:23:42Z","timestamp":1790166222531,"version":"4.1.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"European University Cyprus"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Oral Invest"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the diagnostic accuracy of the Diagnocat\u2122 artificial intelligence (AI) system for caries detection on bitewing radiographs compared with expert human examiners, with emphasis on differences between enamel and dentin lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A sample of 100 digital bitewing radiographs (1540 surfaces) was retrospectively selected from the European University Cyprus dental clinic database using a systematic backward screening method. Radiographs were obtained with a standardized phosphor plate system and anonymized before analysis. Two independent experts (operative dentistry and oral radiology) established the reference standard. AI and human assessments were binarized (caries\/no caries; enamel\/dentin) and compared. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, and statistical significance was tested across detection categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Diagnocat\u2122 showed high specificity (94.3%, 95% CI: 92.4%\u201396.0%) and NPV (96.1%, 95% CI: 94.7%\u201397.3%), with an overall accuracy of 91.6% (95% CI: 89.7%\u201393.4%). Sensitivity was moderate (73.1%, 95% CI: 65.9%\u201379.9%), and PPV was 64.7% (95% CI: 57.7%\u201371.5%). Agreement with the expert consensus was substantial (Cohen\u2019s \u03ba\u2009=\u20090.638). For enamel lesions, sensitivity and specificity were 73.3% (95% CI: 62.8%\u201382.7%) and 92.9% (95% CI: 91.0%\u201394.7%) with moderate agreement with the consensus (Cohen\u2019s \u03ba\u2009=\u20090.492) and for dentin lesions they were 72.8% (95% CI: 61.8%\u201383.8%) and 92.8% (95% CI 90.9%\u201394.6%) with moderate agreement with the consensus (Cohen\u2019s \u03ba\u2009=\u20090.468). NPV remained high (\u2265\u200998.0%), while PPV was low (42.0% and 39.2%), across lesion types. Detection patterns differed significantly between AI and the reference standard (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Diagnocat\u2122 demonstrated good diagnostic performance in ruling out caries. However, its overall lower sensitivity emphasizes the need for clinician oversight, especially in detecting early-stage disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance<\/jats:title>\n                    <jats:p>This study offers an independent validation of Diagnocat\u2122 using bitewing radiographs. It demonstrates lesion-depth\u2013specific insights, showing that while AI is highly reliable for excluding disease, its predictive value remains limited.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00784-026-06882-z","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:48:09Z","timestamp":1777430889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs"],"prefix":"10.1007","volume":"30","author":[{"given":"Kostis","family":"Giannakopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Argyro","family":"Kavadella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Paraskevis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristidis","family":"Arhakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miltiadis A.","family":"Makrygiannakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleftherios G.","family":"Kaklamanos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"issue":"12","key":"6882_CR1","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.3390\/bioengineering11121267","volume":"11","author":"SK Mallineni","year":"2024","unstructured":"Mallineni SK, Sethi M, Punugoti D, Kotha SB, Alkhayal Z, Mubaraki S, Almotawah FN, Kotha SL, Sajja R, Nettam V, Thakare AA, Sakhamuri S (2024) Artificial Intelligence in Dentistry: A Descriptive Review. 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