See the published version of this preprint at Scientific Reports.
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Research Article
Kaan Orhan
Ankara University
Corresponding Author
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Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious and challenging for the untrained eye. This is due to additional training, specific knowledge and time required for analysis and diagnosis. When compared to conventional dental imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates analysis and diagnosis. This system is based on deep learning approaches that can localize teeth and define pathologies within three-dimensional CBCT scans. The study showed that the diagnostic performance of AI system image interpretation reaches and sometimes exceeds in comparison to clinician’s expertise. In this randomized cross-over trial we demonstrated a significant improvement of aided diagnostic accuracy for various dental diseases in comparison to a group of radiologists that made unaided decisions. AI can be used for both stand-alone CBCT interpretation and as a decision support system to improve quality of diagnostics and time efficiency.
Keywords
Artificial intelligence, deep learning, tooth detection, CBCT, dentomaxillofacial imaging
Figure 1
Competing interest reported. Competing Interests Financial support was received by Diagnocat Co. Ltd., San Franscico CA. Matvey Ezhov, Maxim Gusarev, Maria Golitsyna, Eugene Shumilov, and Alex Sanders are employees of Diagnocat Co. Ltd. Julian M Yates, Evgeny Kushnerev, Dania Tamimi, Secil Aksoy, and Kaan Orhan declare no potential conflict of interest.
This is a list of supplementary files associated with this preprint. Click to download.
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View the published article at Scientific Reports.
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Posted 19 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 23 Apr, 2021
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Received 09 Apr, 2021
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On 05 Apr, 2021
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On 05 Apr, 2021
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Invitations sent on 05 Apr, 2021
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See the published version of this preprint at Scientific Reports.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Kaan Orhan
Ankara University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Scientific Reports.
Version 1
Posted 19 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 23 Apr, 2021
Reviews received
Received 09 Apr, 2021
Reviewers agreed
On 05 Apr, 2021
Reviewers agreed
On 05 Apr, 2021
Reviewers invited
Invitations sent on 05 Apr, 2021
Editor assigned
On 05 Apr, 2021
Editor invited
On 18 Mar, 2021
Submission checks complete
On 18 Mar, 2021
First submitted
On 06 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Scientific Reports.
Cone-beam computed tomography (CBCT) in dental practice is becoming increasingly popular. However, the correct teeth identification, positioning and diagnosis based on CBCT can be tedious and challenging for the untrained eye. This is due to additional training, specific knowledge and time required for analysis and diagnosis. When compared to conventional dental imaging methods. In this study, we introduce a novel artificial intelligence (AI) system that facilitates analysis and diagnosis. This system is based on deep learning approaches that can localize teeth and define pathologies within three-dimensional CBCT scans. The study showed that the diagnostic performance of AI system image interpretation reaches and sometimes exceeds in comparison to clinician’s expertise. In this randomized cross-over trial we demonstrated a significant improvement of aided diagnostic accuracy for various dental diseases in comparison to a group of radiologists that made unaided decisions. AI can be used for both stand-alone CBCT interpretation and as a decision support system to improve quality of diagnostics and time efficiency.
Figure 1
Competing interest reported. Competing Interests Financial support was received by Diagnocat Co. Ltd., San Franscico CA. Matvey Ezhov, Maxim Gusarev, Maria Golitsyna, Eugene Shumilov, and Alex Sanders are employees of Diagnocat Co. Ltd. Julian M Yates, Evgeny Kushnerev, Dania Tamimi, Secil Aksoy, and Kaan Orhan declare no potential conflict of interest.
This is a list of supplementary files associated with this preprint. Click to download.
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