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- Title
Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging.
- Authors
Amasya, Hakan; Alkhader, Mustafa; Serindere, Gözde; Futyma-Gąbka, Karolina; Aktuna Belgin, Ceren; Gusarev, Maxim; Ezhov, Matvey; Różyło-Kalinowska, Ingrid; Önder, Merve; Sanders, Alex; Costa, Andre Luiz Ferreira; Castro Lopes, Sérgio Lúcio Pereira de; Orhan, Kaan
- 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.
- Subjects
SAN Francisco (Calif.); DECISION support systems; CONVOLUTIONAL neural networks; DEEP learning; DENTAL caries; CONE beam computed tomography
- Publication
Diagnostics (2075-4418), 2023, Vol 13, Issue 22, p3471
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics13223471