ArticleNature communications2022
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 4 of them syntheses that pooled it.
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Who cites it
109 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.BMC oral health · 2025Pooled it
- Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis.Dento maxillo facial radiology · 2025Pooled it
- Changes in soft tissue dimensions following horizontal guided bone regeneration with a split-thickness flap design - evaluation of 8 cases with a digital method.Head & face medicine · 2024Trial
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Deep learning cone-beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects.Journal of periodontology · 2026Article
- Deep learning-enhanced detection of dental restorations and orthodontic appliances in panoramic radiographs using a clinically annotated dataset.Acta odontologica Scandinavica · 2026Article
- Automated prediction of hand-wrist maturation stage from lateral cephalometric and panoramic radiographs using a deep learning approach.European journal of orthodontics · 2026Article
- A multimodal vision-language model for comprehensive dental diagnosis and enhanced clinical practice.Nature communications · 2026Article
- Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.Clinical oral investigations · 2026Article
- YOLO11-guided swin transformer for molar occlusion classification.Scientific reports · 2026Article
- Automated Adaptive Approach for Specular Highlight Removal in Digital Dentistry: A Proof-of-Concept Study for Preserving Tooth Surface Texture.Journal of clinical medicine · 2026Article
- [Research on automated orthodontic tooth arrangement by integrating multi-source structural information and dental arch prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Automated micro-CT quantification of clear aligner fit: a pilot comparison of manufacturing processes.BMC oral health · 2026Article
- Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care.International dental journal · 2026Review
- Review
- AI-Assisted Identification of the Medial Lingual Foramen on CBCT: A Deep Learning Approach for Preoperative Implant Assessment.Medicina (Kaunas, Lithuania) · 2026Article
- Deep learning-based tooth axis estimation from 3D tooth crowns using quaternion representation and multi-loss optimization.Scientific reports · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
49 more citing papers are in PubMed but not listed here.
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16 authors.
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Abstract
Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.
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