ArticleBMC cancer2025
Artificial intelligence based vision transformer application for grading histopathological images of oral epithelial dysplasia: a step towards AI-driven diagnosis.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence-Assisted Histopathologic Diagnosis and Grading of Oral Epithelial Dysplasia: A Systematic Review and Functional Meta-synthesis.Head and neck pathology · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Generalizability of Deep Learning Architectures for Oral Squamous Cell Carcinoma Classification in Histopathology: A Comparative CNN-Vision Transformer Study with External Validation and Label-Quality Analysis.Journal of imaging informatics in medicine · 2026Article
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- Biopsy for Suspicious Oral Lesions: A Review From the American Head and Neck Society-Cancer Prevention Service.Head & neck · 2026Review
- Deep visual detection system for oral squamous cell carcinoma.Scientific reports · 2026Article
- Comprehensive benchmarking of deep learning architectures for multiclass histopathological classification of oral epithelial lesions.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to classify dysplastic and healthy oral epithelial histopathological images, according to WHO and binary grading systems, using the Vision Transformer (ViT) deep learning algorithm-a state-of-the-art Artificial Intelligence (AI) approach and compare it with established Convolutional Neural Network models (VGG16 and ConvNet).
methodsA total of 218 histopathological slide images were collected from the Department of Oral and Maxillofacial Pathology at Tehran University of Medical Sciences archive and combined with two online databases. Two oral pathologists independently labeled the images based on the 2022 World Health Organization (WHO) grading system (mild, moderate and severe), the binary grading system (low risk and high risk), including an additional normal tissue class. After preprocessing, the images were fed to the ViT, VGG16 and ConvNet models.
resultsImage preprocessing yielded 2,545 low-risk, 2,054 high-risk, 726 mild, 831 moderate, 449 severe, and 937 normal tissue patches. The proposed ViT model outperformed both CNNs with the accuracy of 94% (VGG16:86% and ConvNet: 88%) in 3-class scenario and 97% (VGG16:79% and ConvNet: 88%) in 4-class scenario.
conclusionsThe ViT model successfully classified oral epithelial dysplastic tissues with a high accuracy, paving the way for AI to serve as an adjunct or independent tool alongside oral and maxillofacial pathologists for detecting and grading oral epithelial dysplasia.
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