ArticleBioinformation2026
AI-assisted image analysis of
Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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Authors and funding
6 authors.
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Abstract
Premalignant and malignant oral lesions pose a significant global health burden, with delayed diagnosis adversely affecting patient outcomes. Therefore, it is of interest to evaluate the diagnostic performance and reliability of an artificial intelligence (AI)-based image analysis system in identifying early morphological changes in cultured oral epithelial cells under simulated pathological conditions. Human oral keratinocytes were exposed to oxidative stress and inflammatory cytokines and phase-contrast images were analyzed using a convolutional neural network trained to classify normal, stressed and dysplastic cells. The AI system demonstrated high accuracy (94.2%), sensitivity (93.8%), specificity (95.1%) and strong agreement with expert cytopathologists (κ = 0.91), with reduced analysis time compared to manual evaluation. AI-based image analysis shows promise as a reliable adjunctive tool for early detection and screening of oral lesions.
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Registered trials
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