ArticleBMC oral health2024
Convolutional neural network for automated tooth segmentation on intraoral scans.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Effect of 3D superimposition software on intraoral scanner accuracy and monitoring outcome.Clinical oral investigations · 2026Article
- Photo-Based Color Analysis in Restorative Dentistry: The Role of Artificial Intelligence Algorithms.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026Article
- 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
- Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.Clinical oral implants research · 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
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Deep Learning-Based Panoramic Radiograph Retrieval from Antemortem Images for Forensic Identification.International journal of legal medicine · 2026Article
- Automating virtual dental implant planning: can artificial intelligence match clinical expertise?Dento maxillo facial radiology · 2026Article
- Oral health status and associated factors among rural older adults in China using a 3D intraoral scanner: a population-based cross-sectional study in four provinces.BMC public health · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Development of an AI model to predict tooth movement during orthodontic treatment.Bioinformation · 2026Article
- Validation of a commercial intraoral auto-segmentation algorithm against expert-annotated clinical data with comparison to a transparent algorithm.Frontiers in oral health · 2026Article
- Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management.Dentistry journal · 2025Review
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence.Clinical oral investigations · 2025Article
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 2025Article
- A Global-Local Attention Model for 3D Point Cloud Segmentation in Intraoral Scanning: A Novel Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry.Dentistry journal · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
backgroundTooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propose and evaluate the performance of a convolutional neural network (CNN) model for automatic tooth segmentation on IOS images.
methodsA dataset of 761 IOS images (380 upper jaws, 381 lower jaws) was acquired using an intraoral scanner. The inclusion criteria included a full set of permanent teeth, teeth with orthodontic brackets, and partially edentulous dentition. A multi-step 3D U-Net pipeline was designed for automated tooth segmentation on IOS images. The model's performance was assessed in terms of time and accuracy. Additionally, the model was deployed on an online cloud-based platform, where a separate subsample of 18 IOS images was used to test the clinical applicability of the model by comparing three modes of segmentation: automated artificial intelligence-driven (A-AI), refined (R-AI), and semi-automatic (SA) segmentation.
resultsThe average time for automated segmentation was 31.7 ± 8.1 s per jaw. The CNN model achieved an Intersection over Union (IoU) score of 91%, with the full set of teeth achieving the highest performance and the partially edentulous group scoring the lowest. In terms of clinical applicability, SA took an average of 860.4 s per case, whereas R-AI showed a 2.6-fold decrease in time (328.5 s). Furthermore, R-AI offered higher performance and reliability compared to SA, regardless of the dentition group.
conclusionsThe 3D U-Net pipeline was accurate, efficient, and consistent for automatic tooth segmentation on IOS images. The online cloud-based platform could serve as a viable alternative for IOS segmentation.
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