ArticleBMC cancer2025
"Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".
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 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.BMC cancer · 2026Pooled it
- AI-assisted image analysis ofBioinformation · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral 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.
methodsA 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.
resultsAmong 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.
conclusionAI-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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.