ArticleJournal of systems science and complexity2023
Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs.
Article in Journal of systems science and complexity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Evaluation of Hybrid Attention-CNN and Vision Transformer Models for Multi-Class Classification of Third-Second Molar Relationships on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-assisted creation of a large-scale cone beam computed tomography dataset for the segmentation of impacted mandibular third molar.Quantitative imaging in medicine and surgery · 2026Article
- AI-powered precision in dental radiographic analysis using tailored CNNs for tooth numbering and cavity detection.PLOS digital health · 2025Article
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
- A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography.Scientific reports · 2025Article
- Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans.Journal of endodontics · 2024Article
- Article
- STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model.NPJ digital medicine · 2023Article
- Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls.Diagnostics (Basel, Switzerland) · 2023Article
- Dental panoramic X-ray image segmentation for multi-feature coordinate position learning.Digital healthArticle
- [Efficient dental panoramic radiographic diagnosis under resource constraintsNan fang yi ke da xue xue bao = Journal of Southern Medical UniversityArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Panoramic radiographs can assist dentist to quickly evaluate patients' overall oral health status. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application.
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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.