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.
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Posted 19 Mar, 2021
Received 09 Apr, 2021
Received 09 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
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On 05 Apr, 2021
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Invitations sent on 05 Apr, 2021
On 05 Apr, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 06 Mar, 2021
Posted 19 Mar, 2021
Received 09 Apr, 2021
Received 09 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
On 05 Apr, 2021
Invitations sent on 05 Apr, 2021
On 05 Apr, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 06 Mar, 2021
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
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