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Matvey Ezhov, Maxim Gusarev, Maria Golitsyna, Julian M. Yates, Evgeny Kushnerev, Dania Tamimi, Seçil Aksoy, Eugene Shumilov, Alex Sanders, Kaan Orhan
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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Ezhov, M., Gusarev, M., Golitsyna, M., Yates, J. M., Kushnerev, E., Tamimi, D., Aksoy, S., Shumilov, E., Sanders, A., & Orhan, K. (2021). Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Scientific Reports, 11(1), 15006. https://doi.org/10.1038/s41598-021-94093-9
Ezhov, Matvey, et al. "Clinically applicable artificial intelligence system for dental diagnosis with CBCT." Scientific Reports, vol. 11, no. 1, 2021, pp. 15006, https://doi.org/10.1038/s41598-021-94093-9.
Ezhov, Matvey, Maxim Gusarev, Maria Golitsyna, Julian M. Yates, Evgeny Kushnerev, Dania Tamimi, Seçil Aksoy, Eugene Shumilov, Alex Sanders, and Kaan Orhan. "Clinically applicable artificial intelligence system for dental diagnosis with CBCT." Scientific Reports 11, no. 1 (2021): 15006. https://doi.org/10.1038/s41598-021-94093-9.
Ezhov M., Gusarev M., Golitsyna M. [и др.] Clinically applicable artificial intelligence system for dental diagnosis with CBCT // Scientific Reports. — 2021. — Vol. 11, № 1. — P. 15006. — URL: https://doi.org/10.1038/s41598-021-94093-9
@article{ezhov2021clinically,
title = {{Clinically applicable artificial intelligence system for dental diagnosis with CBCT}},
author = {Matvey Ezhov and Maxim Gusarev and Maria Golitsyna and Julian M. Yates and Evgeny Kushnerev and Dania Tamimi and Seçil Aksoy and Eugene Shumilov and Alex Sanders and Kaan Orhan},
journal = {Scientific Reports},
year = {2021},
volume = {11},
number = {1},
pages = {15006},
doi = {10.1038/s41598-021-94093-9},
url = {https://doi.org/10.1038/s41598-021-94093-9}
}TY - JOUR AU - Ezhov, Matvey AU - Gusarev, Maxim AU - Golitsyna, Maria AU - Yates, Julian M. AU - Kushnerev, Evgeny AU - Tamimi, Dania AU - Aksoy, Seçil AU - Shumilov, Eugene AU - Sanders, Alex AU - Orhan, Kaan TI - Clinically applicable artificial intelligence system for dental diagnosis with CBCT JO - Scientific Reports VL - 11 IS - 1 SP - 15006 PY - 2021 DA - 2021/07/22 DO - 10.1038/s41598-021-94093-9 UR - https://doi.org/10.1038/s41598-021-94093-9 ER -
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