ReviewDiagnostics (Basel, Switzerland)2023
The Application of Deep Learning on CBCT in Dentistry.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 2026Article
- Automatic recognition and measurement of anatomical structures associated with the elevation of the maxillary sinus floor by deep learning on cone-beam computed tomographic scans.BMC oral health · 2026Article
- Deep Learning Analysis of CBCT Images for Periodontal Disease: Phenotype-Level Concordance with Independent Transcriptomic and Microbiome Datasets.Dentistry journal · 2025Article
- CBCT: knowledge, attitude, and practice among dentists.BMC oral health · 2025Article
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
- Anatomical classification and geometric modeling of the lingual canal in the anterior mandible: a cone-beam computed tomography study in a Northern Chinese population.Surgical and radiologic anatomy : SRA · 2025Article
- Deep learning for automated dental plaque index assessment: validation against expert evaluations.BMC oral health · 2025Article
- Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.BMC oral health · 2025Article
- Estimating the Severity of Oral Lesions Via Analysis of Cone Beam Computed Tomography Reports: A Proposed Deep Learning Model.International dental journal · 2025Article
- Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study.Journal of imaging informatics in medicine · 2024Article
- Automatic jawbone structure segmentation on dental CBCT images via deep learning.Clinical oral investigations · 2024Article
- A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs.BMC oral health · 2024Article
- Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- A novel approach to craniofacial analysis using automated 3D landmarking of the skull.Scientific reports · 2024Article
- Dental Caries Detection and Classification in CBCT Images Using Deep Learning.International dental journal · 2024Article
- Automated 3D Landmarking of the Skull: A Novel Approach for Craniofacial Analysis.bioRxiv : the preprint server for biology · 2024Article
- Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet.Frontiers in oncology · 2024Article
- Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.
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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.