ArticleInternational journal of oral science2024
Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images.
Article in International journal of oral science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 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
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- A novel fluoride tray-assisted workflow for artificial intelligence-driven automated maxillary gingival segmentation on cone beam CT.Dento maxillo facial radiology · 2026Article
- Deep learning cone-beam computed tomography image segmentation for the 3D visualization of mandibular infraosseous periodontal defects.Journal of periodontology · 2026Article
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- Multi-view deep fusion for forensic dental age estimation in tomographic imaging.International journal of legal medicine · 2026Article
- Evaluating the accuracy of sex estimation from human tooth volume: leveraging automated AI segmentation and comparative analysis of machine learning algorithms.Head & face medicine · 2026Article
- Deep learning-based tooth axis estimation from 3D tooth crowns using quaternion representation and multi-loss optimization.Scientific reports · 2026Article
- Artificial intelligence in healthcare, dentistry, and orthodontics; is it an opportunity or a threat? An epistemological review.Philosophy, ethics, and humanities in medicine : PEHM · 2026Review
- Clinically applicable deep learning model for segmentation of the mandibular bone and inferior alveolar canal in CBCT cross-sectional images.BMC oral health · 2026Article
- Personalized dentistry: Enhancing outcomes through patient-centered innovation.Bioinformation · 2026Article
- Study on the reproducibility of the Frankfort horizontal plane based on CBCT 3D reconstruction and comparative analysis with lateral radiographs.Frontiers in surgery · 2026Article
- The Segmentation and 3D Reconstruction of Teeth, Root Canals, and Alveolar Bones in CBCT Images Based on AVIZO Software.International journal of dentistry · 2026Article
- [Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Review
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in dental radiology: a narrative review.Annals of medicine and surgery (2012) · 2025Review
- A platform combining automatic segmentation and automatic measurement of the maxillary sinus and adjacent structures.Clinical oral investigations · 2025Article
- Clinically oriented automatic three-dimensional enamel segmentation via deep learning.BMC oral health · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.