Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images

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dc.authorid0000-0002-6429-4197
dc.contributor.authorGüneç, Hüseyin Gürkan
dc.contributor.authorÜrkmez, Elif Şeyda
dc.contributor.authorDanacı, Aleyna
dc.contributor.authorDilmaç, Eda
dc.contributor.authorOnay, Hüseyin Hamza
dc.contributor.authorCesur Aydın, Kader
dc.date.accessioned2023-11-27T08:28:22Z
dc.date.available2023-11-27T08:28:22Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi Ana Bilim Dalı
dc.description.abstractBackground: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions: Regarding the evaluation time needed, AI performed faster, on average.
dc.description.sponsorshipMedipol Universityen_US
dc.identifier.citationGüneç, H. G., Ürkmez, E. Ş., Danacı, A., Dilmaç, E., Onay, H. H. ve Cesur Aydın, K. (2023). Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images. Quantitative Imaging in Medicine and Surgery, 13(11), 7494-7503. https://dx.doi.org/10.21037/qims-23-762
dc.identifier.doi10.21037/qims-23-762
dc.identifier.endpage7503
dc.identifier.issn2223-4292
dc.identifier.issn2223-4306
dc.identifier.issue11
dc.identifier.pmid37969638
dc.identifier.scopus2-s2.0-85176261749
dc.identifier.scopusqualityQ2
dc.identifier.startpage7494
dc.identifier.urihttps://dx.doi.org/10.21037/qims-23-762
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11877
dc.identifier.volume13
dc.identifier.wos001077082000001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCesur Aydın, Kader
dc.language.isoen
dc.publisherAME Publishing Company
dc.relation.ispartofQuantitative Imaging in Medicine and Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArtificial Intelligence (AI)
dc.subjectCaries
dc.subjectDental Radiology
dc.subjectDiagnosis
dc.subjectInfection
dc.titleComparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images
dc.typeArticle

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