Background: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

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.

Objectives: The aim of this study was to evaluate and summarise features of currently and formerly available cone beam CT (CBCT) devices from 1996 to 2019. Additionally, a recommendation for standardised reporting of CBCT characteristics was provided. Methods and materials: Information about the features of all available CBCT devices was obtained from the manufacturers’ available data. Moreover, site visits to newly developed CBCT machines’ manufacturers were performed in order to obtain relevant information. Results: A total of 279 CBCT models from 47 manufacturers located in 12 countries (Brazil, China, Denmark, Finland, France, Germany, Italy, Japan, Republic of Korea, Slovakia, Thailand, and USA) could be listed. Overall, wide variations in CBCT features and technical specifications were identified. Conclusions: CBCT in dentomaxillofacial radiology is a generic term applicable to a broad range of CBCT machines and features. Experimental outcomes and literature statements regarding radiation doses, imaging performance and diagnostic applicability of dental CBCT cannot be simply transferred from one CBCT model to another considering a wide variation in technical characteristics and clinical diagnostic performance. The information tabulated in the present study will be later provided on the International Association of DentoMaxilloFacial Radiology website ( www.iadmfr.one ).