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

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dc.contributor.authorGunec, Huseyin Gurkan
dc.contributor.authorUrkmez, Elif Seyda
dc.contributor.authorDanaci, Aleyna
dc.contributor.authorDilmac, Eda
dc.contributor.authorOnay, Huseyin Hamza
dc.contributor.authorAydin, Kader Cesur
dc.date.accessioned2024-03-13T10:33:08Z
dc.date.available2024-03-13T10:33:08Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
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.en_US
dc.identifier.doi10.21037/qims-23-762
dc.identifier.endpage7503en_US
dc.identifier.issn2223-4292
dc.identifier.issn2223-4306
dc.identifier.issue11en_US
dc.identifier.pmid37969638en_US
dc.identifier.scopus2-s2.0-85176261749en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage7494en_US
dc.identifier.urihttps://doi.org/10.21037/qims-23-762
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3787
dc.identifier.volume13en_US
dc.identifier.wosWOS:001153316500007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAme Publishing Companyen_US
dc.relation.ispartofQuantitative Imaging In Medicine And Surgeryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDental radiologyen_US
dc.subjectdiagnosisen_US
dc.subjectartificial intelligence (AI)en_US
dc.subjectcariesen_US
dc.subjectinfectionen_US
dc.titleComparison of artificial intelligence vs. junior dentists' diagnostic performance based on caries and periapical infection detection on panoramic imagesen_US
dc.typeArticleen_US

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