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. 2021 Jul 22;11(1):15006.
doi: 10.1038/s41598-021-94093-9.

Clinically applicable artificial intelligence system for dental diagnosis with CBCT

Matvey Ezhov  1 , Maxim Gusarev  1 , Maria Golitsyna  1 , Julian M Yates  2 , Evgeny Kushnerev  2 , Dania Tamimi  3 , Secil Aksoy  4 , Eugene Shumilov  1 , Alex Sanders  1 , Kaan Orhan  5   6
Affiliations

Clinically applicable artificial intelligence system for dental diagnosis with CBCT

Matvey Ezhov et al. Sci Rep. .

Erratum in

Abstract

In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

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Conflict of interest statement

Financial support was received by Diagnocat Co. Ltd., San Francisco CA. Matvey Ezhov, Maxim Gusarev, Maria Golitsyna, Eugene Shumilov, and Alex Sanders are employees of Diagnocat Co. Ltd. Kaan Orhan is a scientific research advisor for the Diagnocat Co. Ltd., San Francisco CA. Julian M Yates, Evgeny Kushnerev, Dania Tamimi, Secil Aksoy have no potential competing interests.

Figures

Figure 1
Figure 1
Flow diagram of CBCT processing pipeline and workflow of the AI system.
Figure 2
Figure 2
Caries lesion localization mask at the sagittal slice of tooth 17.
Figure 3
Figure 3
Caries lesion at mesiodistal and axial slices of tooth 45. No caries predicted by caries localization module. The identification of caries was overlooked due to metallic artifacts which is an example of incorrect classification by the AI system.
Figure 4
Figure 4
Periapical lesion localization mask at tooth 13.
Figure 5
Figure 5
Incorrect periapical lesion localization mask at tooth 45. The lesion was predicted at the adjacent tooth 46.
See this image and copyright information in PMC

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