ArticleCureus2024
Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Capturing of interdental areas with digital and conventional impression systems and their impact on automatic tooth segmentation in CAD software: an in vitro study.Clinical oral investigations · 2026Article
- Performance assessment of five artificial intelligence-based algorithms for automated tooth segmentation and labeling on intraoral scans.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
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2 authors.
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Abstract
Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Kesling setup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.
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