ArticleTurkish journal of orthodontics2026
Assessing the Reliability of Artificial Intelligence-Based Automatic Digital Model Analysis Compared with Manual Approaches.
Article in Turkish journal of orthodontics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study was conducted to compare the accuracy and reliability of artificial intelligence (AI)-supported automatic digital (AD) model analysis systems with manual digital (MD) and manual plaster (MP) methods in orthodontic diagnosis. Methods: Twenty-four individuals (8 male, 16 female; mean age 19.5±6.05 years) with permanent dentition and mild to moderate crowding were included. Linear measurements were performed using digital calipers on plaster models. Digital models were obtained through intraoral scanning and analysed using OrthoSystem Software (3Shape, Copenhagen, Denmark) with both manual and automatic segmentation. Mesiodistal tooth dimensions, arch length deficiency were evaluated across all three methods, and Bolton analysis. Arch width measurements were compared only between the digital and MP methods due to software limitations in the automatic detection of landmarks. Statistical analysis included repeated-measures analysis of variance, Friedman's test, and paired t-tests with significance set at p<0.05. Results: All three methods demonstrated consistent results with low standard deviations and narrow confidence intervals. Significant differences were found in mesiodistal tooth measurements between digital systems and plaster models, excluding premolars (p<0.05). No significant differences were observed between AD and MD systems for arch length deficiency or Bolton discrepancy (p>0.05). The MP method yielded higher measurements than those from digital systems, suggesting a higher error rate attributable to conventional phase redundancy. Conclusion: Artificial intelligence-based AD model analysis provides reliable, consistent measurements comparable to those of MD methods. The automatic segmentation approach, enhanced by deep learning algorithms, demonstrates clinical accuracy approaching that of MD analysis, offering a time-efficient alternative for orthodontic model assessment.
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