ArticleBMC oral health2023
Intra-oral scan segmentation using deep learning.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Effect of 3D superimposition software on intraoral scanner accuracy and monitoring outcome.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
- Artificial Intelligence-Driven Segmentation of Three Oral Diseases: Enabling Precision Diagnosis and Decision Support.International dental journal · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography.Scientific reports · 2025Article
- A Global-Local Attention Model for 3D Point Cloud Segmentation in Intraoral Scanning: A Novel Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation.Clinical oral investigations · 2025Article
- AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments-a narrative review.Frontiers in dental medicine · 2025Review
- Biomechanical considerations in RPD design: application and perspective of finite element method in distal extension removable partial denture rehabilitation.Frontiers in dental medicine · 2025Review
- DUCU: a conceptual framework for AI-driven conversion of smile design to functional wax-up.Frontiers in oral health · 2025Article
- Recent Advances in Intraoral Scanners.Journal of dental research · 2024Review
- AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy.Scientific reports · 2024Article
- Oral screening of dental calculus, gingivitis and dental caries through segmentation on intraoral photographic images using deep learning.BMC oral health · 2024Article
- The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.BMC medical imaging · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIntra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning. MATERIAL AND
methodsAs a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions.
resultsThe model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges.
conclusionThe proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans. CLINICAL SIGNIFICANCE: Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.
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