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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MetadadosDescriçãoIdioma
Autor(es): dc.contributorIstanbul University-Cerrahpaşa-
Autor(es): dc.contributorHealth Biotechnology Joint Research and Application Center of Excellence-
Autor(es): dc.contributorJordan University of Science and Technology-
Autor(es): dc.contributorMustafa Kemal University-
Autor(es): dc.contributorMedical University of Lublin-
Autor(es): dc.contributorInc-
Autor(es): dc.contributorAnkara University-
Autor(es): dc.contributorCruzeiro do Sul University (UNICSUL)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorAnkara University Medical Design Application-
Autor(es): dc.contributorSemmelweis University-
Autor(es): dc.creatorAmasya, Hakan-
Autor(es): dc.creatorAlkhader, Mustafa-
Autor(es): dc.creatorSerindere, Gözde-
Autor(es): dc.creatorFutyma-Gąbka, Karolina-
Autor(es): dc.creatorAktuna Belgin, Ceren-
Autor(es): dc.creatorGusarev, Maxim-
Autor(es): dc.creatorEzhov, Matvey-
Autor(es): dc.creatorRóżyło-Kalinowska, Ingrid-
Autor(es): dc.creatorÖnder, Merve-
Autor(es): dc.creatorSanders, Alex-
Autor(es): dc.creatorCosta, Andre Luiz Ferreira-
Autor(es): dc.creatorCastro Lopes, Sérgio Lúcio Pereira de-
Autor(es): dc.creatorOrhan, Kaan-
Data de aceite: dc.date.accessioned2025-08-21T20:44:13Z-
Data de disponibilização: dc.date.available2025-08-21T20:44:13Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/diagnostics13223471-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307047-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307047-
Descrição: dc.descriptionThis 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.-
Descrição: dc.descriptionDepartment of Oral and Maxillofacial Radiology Faculty of Dentistry Istanbul University-Cerrahpaşa-
Descrição: dc.descriptionCAST (Cerrahpasa Research Simulation and Design Laboratory) Istanbul University-Cerrahpaşa-
Descrição: dc.descriptionHealth Biotechnology Joint Research and Application Center of Excellence-
Descrição: dc.descriptionDepartment of Oral Medicine and Oral Surgery Faculty of Dentistry Jordan University of Science and Technology-
Descrição: dc.descriptionDepartment of Oral and Maxillofacial Radiology Faculty of Dentistry Mustafa Kemal University-
Descrição: dc.descriptionDepartment of Dental and Maxillofacial Radiodiagnostics Medical University of Lublin-
Descrição: dc.descriptionDiagnocat Inc-
Descrição: dc.descriptionDepartment of Oral and Maxillofacial Radiology Faculty of Dentistry Ankara University-
Descrição: dc.descriptionPostgraduate Program in Dentistry Cruzeiro do Sul University (UNICSUL), SP-
Descrição: dc.descriptionScience and Technology Institute Department of Diagnosis and Surgery São Paulo State University (UNESP), SP-
Descrição: dc.descriptionResearch Center (MEDITAM) Ankara University Medical Design Application-
Descrição: dc.descriptionDepartment of Oral Diagnostics Faculty of Dentistry Semmelweis University-
Descrição: dc.descriptionScience and Technology Institute Department of Diagnosis and Surgery São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationDiagnostics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcone-beam computed tomography-
Palavras-chave: dc.subjectdecision support systems-
Palavras-chave: dc.subjectdental caries-
Palavras-chave: dc.subjectmachine learning-
Título: dc.titleEvaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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