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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This 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.

Details

Title
Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging
Author
Amasya, Hakan 1   VIAFID ORCID Logo  ; Alkhader, Mustafa 2   VIAFID ORCID Logo  ; Serindere, Gözde 3 ; Futyma-Gąbka, Karolina 4 ; Ceren Aktuna Belgin 3   VIAFID ORCID Logo  ; Gusarev, Maxim 5   VIAFID ORCID Logo  ; Ezhov, Matvey 5 ; Różyło-Kalinowska, Ingrid 4   VIAFID ORCID Logo  ; Önder, Merve 6 ; Sanders, Alex 5 ; Ferreira Costa, Andre Luiz 7   VIAFID ORCID Logo  ; Sérgio Lúcio Pereira de Castro Lopes 8 ; Kaan Orhan 9   VIAFID ORCID Logo 

 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye; [email protected]; CAST (Cerrahpasa Research, Simulation and Design Laboratory), Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye; Health Biotechnology Joint Research and Application Center of Excellence, Istanbul 34220, Türkiye 
 Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan; [email protected] 
 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mustafa Kemal University, Hatay 31060, Türkiye; [email protected] (G.S.); [email protected] (C.A.B.) 
 Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland; [email protected] (K.F.-G.); or [email protected] (I.R.-K.) 
 Diagnocat, Inc., San Francisco, CA 94102, USA; [email protected] (M.G.); [email protected] (M.E.); [email protected] (A.S.) 
 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye; [email protected] 
 Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 08060-070, SP, Brazil; [email protected] 
 Science and Technology Institute, Department of Diagnosis and Surgery, São Paulo State University (UNESP), São José dos Campos 01049-010, SP, Brazil; [email protected] 
 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye; [email protected]; Research Center (MEDITAM), Ankara University Medical Design Application, Ankara 06560, Türkiye; Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1088 Budapest, Hungary 
First page
3471
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2893014854
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.