Related Experiment Video
Updated: Apr 24, 2026
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
Published on: August 5, 2021
Improving interproximal surface reconstruction via artificial intelligence-based tooth segmentation with crown-root
Amir Salloum1, Nabil Ghosn2, Alain Tauk3
1Imaging Unit, Craniofacial Research Laboratory, Faculty of Dental Medicine, Saint-Joseph University of Beirut, Beirut, Lebanon.
Introduction:
Tooth segmentation plays a fundamental role in digital diagnosis and design. The interproximal surface, a key area for accurate tooth segmentation, remains poorly captured due to intraoral scanner (IOS) limitations. The objective of this study was to evaluate the 3-dimensional accuracy of the interproximal surface in a dentition model segmented using the following: (1) IOS alone; (2) artificial intelligence (AI)-based IOS and cone-beam computed tomography (CBCT) fusion; and (3) IOS-CBCT fusion followed by manual interproximal adjustment.
Methods:
Fourteen extracted maxillary teeth were scanned by IOS to be used as a reference, then arranged in a full-arch configuration with moderate crowding. IOS and CBCT segmentation of the model and subsequent fusion of segmented crowns and roots were performed on the Relu AI-based platform (March 2022 version; Relu BV, Leuven, Belgium). The fused models were then manually adjusted at the interproximal surface, as seen from the axial CBCT slices. Three-dimensional deviation analyses were carried out on the 3 segmented models for the interproximal, outer, and total crown surfaces. Statistical analysis was conducted using repeated-measures analysis of variance, Friedman, Tukey honest significant difference, and Wilcoxon signed rank tests (P <0.05).
Results:
Descriptive statistics revealed a tendency for IOS models to overestimate the interproximal surface compared with fused models. The manually adjusted models demonstrated significantly improved interproximal accuracy (P <0.05). The total surface accuracy of fused models was significantly improved from IOS models (P <0.05). Outer surface deviations did not vary.
Conclusions:
The preliminary findings suggest that CBCT integration significantly enhances the total surface accuracy of AI-segmented tooth models, whereas localized manual refinement further improves interproximal trueness.
