ArticleBMC oral health2025
Enhancing furcation involvement classification on panoramic radiographs with vision transformers.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.
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14 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- 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
- Towards accurate occlusal plane positioning in panoramic radiographs: a deep learning-assisted study.Oral radiology · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- Artificial intelligence for detection and classification of furcation defects using radiographic imaging: A systematic review.Imaging science in dentistry · 2025Review
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
- A scientometric study on research trends and characteristics of furcation involvement in periodontal disease.Journal of dental sciences · 2025Article
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures.Journal of orthopaedic surgery and research · 2025Article
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8 authors.
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Abstract
backgroundThe severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs.
methodsA total of 1,568 tooth images obtained from 506 panoramic radiographs were used to construct the database and evaluate the models. This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet.
resultsAmong the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). ViT also recorded the highest area under the curve (AUC) (98%), outperforming the other models with statistically significant differences (p < 0.05), confirming its enhanced classification capability. The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions.
conclusionDL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.
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