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Updated: Sep 15, 2025
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Automated detection and numbering of primary and permanent teeth in digital impressions of children using artificial
Niels van Nistelrooij1, Haline Cunha de Medeiros Maia2, Lingyun Cao3
1Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Objectives:
Considering the importance of distinguishing between primary and permanent teeth in children with mixed dentition, this study aimed to develop and evaluate an automated method for segmenting and labelling primary and permanent teeth in digital impressions.
Methods:
716 digital impressions from 351 patients with primary or mixed dentitions were collected from the Netherlands, Brazil, and the 3DTeethSeg22 challenge dataset. The scans were annotated with tooth segmentations and primary and permanent teeth FDI numbers. A deep learning model was applied that combined large-context predictions for tooth labelling with high-resolution predictions for tooth segmentation. Using the collected scans, the model was trained and evaluated with five-fold cross-validation for tooth detection (F1-score), tooth segmentation (Dice score), and tooth labelling (macro-F1). Additionally, the model was trained and evaluated using the train-test split of the 3DTeethSeg22 challenge dataset.
Results:
The developed model achieved highly effective results for tooth detection (F1-score = 0.996), tooth segmentation (Dice = 0.969), and tooth labelling (macro-F1 = 0.989). Moreover, a digital impression was processed in under two seconds on average. Furthermore, the proposed method outperformed the top-ranked 3DTeethSeg22 challenge submission (score = 0.954 vs. 0.976) and was particularly effective for tooth labelling (tooth identification rate = 0.910 vs. 0.955). Failure cases revealed mistakes for unusual dental conditions or ambiguous tooth eruption patterns.
Conclusions:
A highly effective algorithm for tooth segmentation was developed to differentiate between primary and permanent teeth in digital impressions. This fast and accurate model can benefit dentists in documenting children's teeth during the mixed dentition stage.
Clinical Significance:
The algorithm provides an accurate and reliable tool for AI-assisted identification and numbering of primary and permanent teeth in digital impressions obtained from children with mixed dentition, thereby enhancing clinical workflow, improving treatment planning accuracy, and facilitating communication with patients and caregivers.
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