Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images.

OBJECTIVES: To develop and assess the performance of a novel artificial intelligence (AI)-driven convolutional neural network (CNN)-based tool for automated three-dimensional (3D) maxillary alveolar bone segmentation on cone-beam computed tomography (CBCT) images. MATERIALS AND METHODS: A total of 141 CBCT scans were collected for performing training (n = 99), validation (n = 12), and testing (n = 30) of the CNN model for automated segmentation of the maxillary alveolar bone and its crestal contour. Following automated segmentation, the 3D models with under- or overestimated segmentations were refined by an expert for generating a refined-AI (R-AI) segmentation. The overall performance of CNN model was assessed. Also, 30% of the testing sample was randomly selected and manually segmented to compare the accuracy of AI and manual segmentation. Additionally, the time required to generate a 3D model was recorded in seconds (s). RESULTS: The accuracy metrics of automated segmentation showed an excellent range of values for all accuracy metrics. However, the manual method (95% HD: 0.20 ± 0.05 mm; IoU: 95% ± 3.0; DSC: 97% ± 2.0) showed slightly better performance than the AI segmentation (95% HD: 0.27 ± 0.03 mm; IoU: 92% ± 1.0; DSC: 96% ± 1.0). There was a statistically significant difference of the time-consumed among the segmentation methods (p < .001). The AI-driven segmentation (51.5 ± 10.9 s) was 116 times faster than the manual segmentation (5973.3 ± 623.6 s). The R-AI method showed intermediate time-consumed (1666.7 ± 588.5 s). CONCLUSION: Although the manual segmentation showed slightly better performance, the novel CNN-based tool also provided a highly accurate segmentation of the maxillary alveolar bone and its crestal contour consuming 116 times less than the manual approach.

Performance of novice versus experienced surgeons for dental implant placement with freehand, static guided and dynamic navigation approaches.

Lack of evidence exists related to the investigation of the accuracy and efficacy of novice versus experienced practitioners for dental implant placement. Hence, the following in vitro study was conducted to assess the accuracy of implant positioning and self-efficacy of novice compared to experienced surgeons for placing implant using freehand (FH), pilot drill-based partial guidance (PPG) and dynamic navigation (DN) approaches. The findings revealed that DN significantly improved the angular accuracy of implant placement compared with FH (P < 0.001) and PPG approaches (P < 0.001). The time required with DN was significantly longer than FH and PPG (P < 0.001), however, it was similar for both novice and experienced practitioners. The surgeon's self-confidence questionnaire suggested that novice practitioners scored higher with both guided approaches, whereas experienced practitioners achieved higher scoring with PPG and FH compared to DN. In conclusion, implant placement executed under the guidance of DN showed high accuracy irrespective of the practitioner's experience. The application of DN could be regarded as a beneficial tool for novices who offered high confidence of using the navigation system with the same level of accuracy and surgical time as that of experienced practitioners.

Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks.

OBJECTIVE: To assess the accuracy of a novel Artificial Intelligence (AI)-driven tool for automated detection of teeth and small edentulous regions on Cone-Beam Computed Tomography (CBCT) images. MATERIALS AND METHODS: After AI training and testing with 175 CBCT scans (130 for training and 40 for testing), validation was performed on a total of 46 CBCT scans selected for this purpose. Scans were split into fully dentate and partially dentate patients (small edentulous regions). The AI Driven tool (Virtual Patient Creator, Relu BV, Leuven, Belgium) automatically detected, segmented and labelled teeth and edentulous regions. Human performance served as clinical reference. Accuracy and speed of the AI-driven tool to detect and label teeth and edentulous regions in partially edentulous jaws were assessed. Automatic tooth segmentation was compared to manually refined segmentation and accuracy by means of Intersetion over Union (IoU) and 95% Hausdorff Distance served as a secondary outcome. RESULTS: The AI-driven tool achieved a general accuracy of 99.7% and 99% for detection and labelling of teeth and missing teeth for both fully dentate and partially dentate patients, respectively. Automated detections took a median time of 1.5s, while the human operator median time was 98s (P<0.0001). Segmentation accuracy measured by Intersection over Union was 0.96 and 0.97 for fully dentate and partially edentulous jaws respectively. CONCLUSIONS: The AI-driven tool was accurate and fast for CBCT-based detection, segmentation and labelling of teeth and missing teeth in partial edentulism. CLINICAL SIGNIFICANCE: The use of AI may represent a promising time-saving tool serving radiological reporting, with a major step forward towards automated dental charting, as well as surgical and treatment planning.

Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images - A validation study.

OBJECTIVES: To assess the influence of dental fillings on the performance of an artificial intelligence (AI)-driven tool for tooth segmentation on cone-beam computed tomography (CBCT) according to the type of tooth. METHODS: A total of 175 CBCT scans (500 teeth) were recruited for performing training (140 CBCT scans - 400 teeth) and validation (35 CBCT scans - 100 teeth) of the AI convolutional neural networks. The test dataset involved 74 CBCT scans (226 teeth), which was further divided into control and experimental groups depending on the presence of dental filling: without filling (control group: 24 CBCT scans - 113 teeth) and with coronal and/or root filling (experimental group: 50 CBCT scans - 113 teeth). The segmentation performance for both groups was assessed. Additionally, 10% of each tooth type (anterior, premolar, and molar) was randomly selected for time analysis according to manual, AI-based and refined-AI segmentation methods. RESULTS: The presence of fillings significantly influenced the segmentation performance (p<0.05). However, the accuracy metrics showed an excellent range of values for both control (95% Hausdorff Distance (95% HD): 0.01-0.08 mm; Intersection over union (IoU): 0.97-0.99; Dice similarity coefficient (DSC): 0.98-0.99; Precision: 1.00; Recall: 0.97-0.99; Accuracy: 1.00) and experimental groups (95% HD: 0.17-0.25 mm; IoU: 0.91-0.95; DSC: 0.95-0.97; Precision:1.00; Recall: 0.91-0.95; Accuracy: 0.99-1.00). The time analysis showed that the AI-based segmentation was significantly faster with a mean time of 29.8 s (p<0.001). CONCLUSIONS: The proposed AI-driven tool allowed an accurate and time-efficient approach for the segmentation of teeth on CBCT images irrespective of the presence of high-density dental filling material and the type of tooth. CLINICAL SIGNIFICANCE: Tooth segmentation is a challenging and time-consuming task, mainly in the presence of artifacts generated by dental filling material. The proposed AI-driven tool could offer a clinically acceptable approach for tooth segmentation, to be applied in the digital dental workflows considering its time efficiency and high accuracy regardless of the presence of dental fillings.