Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging

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dc.authoridOrhan, Kaan/0000-0001-6768-0176
dc.authoridFerreira Costa, Andre Luiz/0000-0003-4856-5417
dc.authoridAmasya, Hakan/0000-0001-7400-9938
dc.authoridRozylo-Kalinowska, Ingrid/0000-0001-5162-1382
dc.authoridAlkhader, Mustafa/0000-0002-8475-2386
dc.contributor.authorAmasya, Hakan
dc.contributor.authorAlkhader, Mustafa
dc.contributor.authorSerindere, Goezde
dc.contributor.authorFutyma-Gabka, Karolina
dc.contributor.authorAktuna Belgin, Ceren
dc.contributor.authorGusarev, Maxim
dc.contributor.authorEzhov, Matvey
dc.date.accessioned2024-09-18T20:15:04Z
dc.date.available2024-09-18T20:15:04Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThis study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.en_US
dc.identifier.doi10.3390/diagnostics13223471
dc.identifier.issn2075-4418
dc.identifier.issue22en_US
dc.identifier.pmid37998607en_US
dc.identifier.scopus2-s2.0-85178384377en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13223471
dc.identifier.urihttps://hdl.handle.net/20.500.12483/9431
dc.identifier.volume13en_US
dc.identifier.wosWOS:001121034000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdental cariesen_US
dc.subjectcone-beam computed tomographyen_US
dc.subjectmachine learningen_US
dc.subjectdecision support systemsen_US
dc.titleEvaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imagingen_US
dc.typeArticleen_US

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