Clinically applicable artificial intelligence system for dental diagnosis with CBCT

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 modul...

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Autores principales:	Matvey Ezhov, Maxim Gusarev, Maria Golitsyna, Julian M. Yates, Evgeny Kushnerev, Dania Tamimi, Secil Aksoy, Eugene Shumilov, Alex Sanders, Kaan Orhan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b225240d1f0b411598955245409d2c54
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spelling	oai:doaj.org-article:b225240d1f0b411598955245409d2c542021-12-02T16:26:37ZClinically applicable artificial intelligence system for dental diagnosis with CBCT10.1038/s41598-021-94093-92045-2322https://doaj.org/article/b225240d1f0b411598955245409d2c542021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94093-9https://doaj.org/toc/2045-2322Abstract 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.Matvey EzhovMaxim GusarevMaria GolitsynaJulian M. YatesEvgeny KushnerevDania TamimiSecil AksoyEugene ShumilovAlex SandersKaan OrhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matvey Ezhov Maxim Gusarev Maria Golitsyna Julian M. Yates Evgeny Kushnerev Dania Tamimi Secil Aksoy Eugene Shumilov Alex Sanders Kaan Orhan Clinically applicable artificial intelligence system for dental diagnosis with CBCT
description	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.
format	article
author	Matvey Ezhov Maxim Gusarev Maria Golitsyna Julian M. Yates Evgeny Kushnerev Dania Tamimi Secil Aksoy Eugene Shumilov Alex Sanders Kaan Orhan
author_facet	Matvey Ezhov Maxim Gusarev Maria Golitsyna Julian M. Yates Evgeny Kushnerev Dania Tamimi Secil Aksoy Eugene Shumilov Alex Sanders Kaan Orhan
author_sort	Matvey Ezhov
title	Clinically applicable artificial intelligence system for dental diagnosis with CBCT
title_short	Clinically applicable artificial intelligence system for dental diagnosis with CBCT
title_full	Clinically applicable artificial intelligence system for dental diagnosis with CBCT
title_fullStr	Clinically applicable artificial intelligence system for dental diagnosis with CBCT
title_full_unstemmed	Clinically applicable artificial intelligence system for dental diagnosis with CBCT
title_sort	clinically applicable artificial intelligence system for dental diagnosis with cbct
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b225240d1f0b411598955245409d2c54
work_keys_str_mv	AT matveyezhov clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT maximgusarev clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT mariagolitsyna clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT julianmyates clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT evgenykushnerev clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT daniatamimi clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT secilaksoy clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT eugeneshumilov clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT alexsanders clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct AT kaanorhan clinicallyapplicableartificialintelligencesystemfordentaldiagnosiswithcbct
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Clinically applicable artificial intelligence system for dental diagnosis with CBCT

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