ArticleClinical oral investigations2024
Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection.
Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Article
- Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study.Scientific reports · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Image-Based Artificial Intelligence for Predicting Malignant Transformation of Oral Potentially Malignant Disorders: A Scoping Review.Journal of clinical medicine · 2026Review
- A Review on Image Processing and Fractal Analysis in Oral Potentially Malignant Disorders.Oral diseases · 2026Review
- A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.BMC oral health · 2026Article
- Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Diagnostic efficacy of VELscope in oral potentially malignant disorders: a systematic review and meta-analysis.Frontiers in oral health · 2026Review
- Molecular and microenvironmental drivers of malignant transformation from oral potentially malignant disorders to oral squamous cell carcinoma.Frontiers in oncology · 2026Review
- A comparative study of single-stage and dual-stage classification models for OPMDs.BMC oral health · 2025Article
- Artificial intelligence in the study of oral lichen planus characteristics: a review.BDJ open · 2025Review
- Article
- Vision transformer-based diagnosis of lumbar disc herniation with grad-CAM interpretability in CT imaging.BMC musculoskeletal disorders · 2025Article
- Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.Clinical oral investigations · 2025Article
- Artificial intelligence and the diagnosis of oral cavity cancer and oral potentially malignant disorders from clinical photographs: a narrative review.Frontiers in oral health · 2025Review
- AI augmented edge and fog computing for Internet of Health Things (IoHT).PeerJ. Computer science · 2025Article
- Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDiagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.
methods4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented.
resultsThe detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938.
conclusionsOSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective. CLINICAL RELEVANCE: Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.
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