ArticleBMC oral health2025
Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in the Radiological Diagnosis of Impacted Maxillary Canines: A Systematic Review.Journal of clinical medicine · 2026Review
- AI and 3D printing-assisted surgical exposure of impacted canines and tooth extraction in complex cleft lip and palate orthodontic treatment.Journal of surgical case reports · 2026Article
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Authors and funding
11 authors.
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Abstract
backgroundImpacted canines are one of the most frequently encountered dental anomalies in maxillofacial practice. Accurate localization of these teeth is crucial for treatment planning, and Cone Beam Computed Tomography (CBCT) offers detailed 3D imaging for this purpose. However, manual segmentation on CBCT scans is time-consuming and subject to inter-observer variability. This study aimed to develop a deep learning model based on nnU-Net v2 for the automatic segmentation of impacted canines and to evaluate its performance using both classification and segmentation metrics.
methodsA total of 159 CBCT scans containing impacted canines were retrospectively collected and annotated using web-based segmentation software. Model training was performed using the nnU-Net v2 architecture with a learning rate of 0.00001 for 1000 epochs. The performance of the model was evaluated using recall and precision. In addition, segmentation performance was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (95% HD in mm), and Intersection over Union (IoU).
resultsThe nnU-Net v2 model achieved high performance in the detection and segmentation of impacted canines. The values obtained for recall and precision 0.90 and 0.82, respectively. The segmentation metrics were also favorable, with a DSC of 0.84, 95% HD of 7.07 mm, and IoU of 0.74, indicating good overlap between predicted and reference segmentations.
conclusionsThe results suggest that the nnU-Net v2-based deep learning model can effectively and autonomously segment impacted canines in CBCT volumes. Its strong performance highlights the potential of artificial intelligence to improve diagnostic efficiency in dentomaxillofacial radiology.
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