ArticleEuropean journal of dentistry2023
Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks.
Article in European journal of dentistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study.Scientific reports · 2026Article
- A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.BMC oral health · 2026Article
- Assessment of artificial intelligence in detecting errors on panoramic radiographs.Imaging science in dentistry · 2026Article
- Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders.BMC oral health · 2026Article
- Challenges and contributing factors in glucocorticoid treatment adherence among patients with oral lichen planus: a cross-sectional study and management strategies.BMC oral health · 2026Article
- A Multimodal Large Language Model Framework for Clinical Subtyping and Malignant Transformation Risk Prediction in Oral Lichen Planus: A Paired Comparison With Expert Clinicians.International dental journal · 2026Article
- Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Diagnostic Efficiency of Artificial Intelligence Integrated Intraoral Mobile Photographs in Identification of Oral Potentially Malignant Disorders: An Umbrella Review.International journal of dentistry · 2026Review
- Article
- Artificial intelligence in the study of oral lichen planus characteristics: a review.BDJ open · 2025Review
- Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.Clinical oral investigations · 2025Article
- Mucoscopic Features of Oral Lichen Planus: A Retrospective Comparative Study with Inflammatory Mimickers.Diagnostics (Basel, Switzerland) · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.BMC oral health · 2025Article
- Integrating design thinking into dental education.Frontiers in oral health · 2025Review
- Artificial intelligence-based diagnosis of oral leukoplakia using deep convolutional neural networks Xception and MobileNet-v2.Frontiers in oral health · 2025Article
- Research and Application of Deep Learning Models with Multi-Scale Feature Fusion for Lesion Segmentation in Oral Mucosal Diseases.Bioengineering (Basel, Switzerland) · 2024Article
- Photobiomodulation Therapy in the Management of Oral Lichen Planus: A Systematic Review and Meta-Analysis.European journal of dentistry · 2024Article
- Classification-Predictive Model Based on Artificial Neural Network Validated by Histopathology and Direct Immunofluorescence for the Diagnosis of Oral Lichen Planus.Diagnostics (Basel, Switzerland) · 2024Article
- Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection.Clinical oral investigations · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to employ artificial intelligence (AI) via convolutional neural network (CNN) for the separation of oral lichen planus (OLP) and non-OLP in biopsy-proven clinical cases of OLP and non-OLP. MATERIALS AND
methodsData comprised of clinical photographs of 609 OLP and 480 non-OLP which diagnosis has been confirmed histopathologically. Fifty-five photographs from the OLP and non-OLP groups were randomly selected for use as the test dataset, while the remaining were used as training and validation datasets. Data augmentation was performed on the training dataset to increase the number and variation of photographs. Performance metrics for the CNN model performance included accuracy, positive predictive value, negative predictive value, sensitivity, specificity, and F1-score. Gradient-weighted class activation mapping was also used to visualize the important regions associated with discriminative clinical features on which the model relies.
resultsAll the selected CNN models were able to diagnose OLP and non-OLP lesions using photographs. The performance of the Xception model was significantly higher than that of the other models in terms of overall accuracy and F1-score.
conclusionsOur demonstration shows that CNN models can achieve an accuracy of 82 to 88%. Xception model performed the best in terms of both accuracy and F1-score.
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Registered trials
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