Three-dimensional evaluation of a virtual setup considering the roots and alveolar bone in molar distalization cases.

We aimed to evaluate root parallelism and the dehiscence or fenestrations of virtual teeth setup using roots isolated from cone beam computed tomography (CBCT) images. Sixteen patients undergoing non-extraction orthodontic treatment with molar distalization were selected. Composite teeth were created by merging CBCT-isolated roots with intraoral scan-derived crowns. Three setups were performed sequentially: crown setup considering only the crowns, root setup-1 considering root alignment, and root setup-2 considering the roots and surrounding alveolar bone. We evaluated the parallelism and exposure of the roots and compared the American Board of Orthodontics Objective Grading System (ABO-OGS) scores using three-dimensionally printed models among the setups. The mean angulation differences between adjacent teeth in root setups-1 and -2 were significantly smaller than in the crown setup, except for some posterior teeth (p < 0.05). The amount of root exposure was significantly smaller in root setup-2 compared to crown setup and root setup-1 except when the mean exposure was less than 0.6 mm (p < 0.05). There was no significant difference in ABO-OGS scores among the setups. Thus, virtual setup considering the roots and alveolar bone can improve root parallelism and reduce the risk of root exposure without compromising occlusion quality.

Reliability and time-based efficiency of artificial intelligence-based automatic digital model analysis system.

OBJECTIVES: To compare the reliability, reproducibility, and time-based efficiency of automatic digital (AD) and manual digital (MD) model analyses using intraoral scan models. MATERIAL AND METHODS: Two examiners analysed 26 intraoral scanner records using MD and AD methods for orthodontic modelling. Tooth size reproducibility was confirmed using a Bland-Altman plot. The Wilcoxon signed-rank test was conducted to compare the model analysis parameters (tooth size, sum of 12-teeth, Bolton analysis, arch width, arch perimeter, arch length discrepancy, and overjet/overbite) for each method, including the time taken for model analysis. RESULTS: The MD group exhibited a relatively larger spread of 95% agreement limits when compared with AD group. The standard deviations of repeated tooth measurements were 0.15 mm (MD group) and 0.08 mm (AD group). The mean difference values of the 12-tooth (1.80-2.38 mm) and arch perimeter (1.42-3.23 mm) for AD group was significantly (P < 0.001) larger than that for the MD group. The arch width, Bolton, and overjet/overbite were clinically insignificant. The overall mean time required for the measurements was 8.62 min and 0.56 min for the MD and AD groups, respectively. LIMITATIONS: Validation results may vary in different clinical cases because our evaluation was limited to mild-to-moderate crowding in the complete dentition. CONCLUSIONS: Significant differences were observed between AD and MD groups. The AD method demonstrated reproducible analysis in a considerably reduced timeframe, along with a significant difference in measurements compared to the MD method. Therefore, AD analysis should not be interchanged with MD, and vice versa.

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic graph convolutional neural network (DGCNN)-based algorithm for automatic tooth segmentation and classification using 516 digital dental models. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based tooth segmentation (LS) using OrthoAnalyzer software, and (3) tooth designation and segmentation (DS) using Autolign software. We evaluated the segmentation success rate, mesiodistal (MD) width, clinical crown height (CCH), and segmentation time. For the AS, LS, and DS, the tooth segmentation success rates were 97.26%, 97.14%, and 87.86%, respectively (p < 0.001, post-hoc; AS, LS > DS), the means of MD widths were 8.51, 8.28, and 8.63 mm, respectively (p < 0.001, post hoc; DS > AS > LS), the means of CCHs were 7.58, 7.65, and 7.52 mm, respectively (p < 0.001, post-hoc; LS > DS, AS), and the means of segmentation times were 57.73, 424.17, and 150.73 s, respectively (p < 0.001, post-hoc; AS < DS < LS). Automatic tooth segmentation of a digital dental model using deep learning showed high segmentation success rate, accuracy, and efficiency; thus, it can be used for orthodontic diagnosis and appliance fabrication.