“Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification”
Bibliographic record
Abstract
BACKGROUND: Oral cancer remains a major global health issue, with timely diagnosis being essential due to its varied clinical presentation. This study explores how artificial intelligence (AI) can support early detection by analyzing intraoral photographs. METHODS: A cross-sectional analysis was performed using 518 intraoral clinical images collected from the Department of Oral Medicine, Kerman Faculty of Dentistry, between 2009 and 2023. The dataset comprised 104 images of malignant lesions and 414 of benign or normal tissue, all confirmed by a specialist in oral pathology. Three pretrained deep learning models, DenseNet-121, EfficientNet-B0, and ResNet-50, were evaluated for their ability to classify lesions as malignant or benign. The data were split into training (80%) and testing (20%) sets, with preprocessing completed before analysis. RESULTS: Among the models, DenseNet-121 demonstrated superior performance, achieving 91% accuracy, 75% sensitivity, 98% specificity, 75% positive predictive value, 96% negative predictive value, an F1 score of 84%, and an area under the curve of 90%. These results exceeded the diagnostic accuracy of an experienced oral specialist. CONCLUSION: AI-based analysis of clinical images can significantly improve early oral cancer detection and should be integrated into clinical workflows to enhance diagnostic precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".