ArticleVirchows Archiv : an international journal of pathology2026
Artificial intelligence for keratosis characterization and identification of lichenoid lesions in histological samples of oral leukoplakia.
Article in Virchows Archiv : an international journal of pathology, 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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Abstract
Oral leukoplakia (OL) is a precancerous condition typically assessed through histopathological examination of mucosal lesion biopsies. Identifying histological features of oral lichenoid lesions (OLL) within OL samples is clinically important, as they influence the risk of malignant transformation and may indicate oral lichen planus (OLP). However, interpretation is challenging, with substantial intra- and inter-observer variability. Artificial intelligence (AI) offers the potential to provide reproducible, objective support for histopathological classification. We developed an AI system to (a) segment histological layers and extract characteristics of the keratinization zone, (b) classify keratinization types, and (c) distinguish OL from OLL. A retrospective cohort of 240 histological slides from 192 patients was included. Of these, 175 transversely sectioned slides underwent manual segmentation of subepithelium, epithelium, keratinization zone, and nuclei in the keratinization zone. Measurements of keratin thickness and nuclei density were performed to classify the keratinization zone into (hyper)orthokeratosis, parakeratosis, or hyperparakeratosis. All 240 slides were labeled as OL or OLL and crops were extracted for diagnosis classification. Segmentation was evaluated with Dice-Sørensen coefficient (DSC), and classification was evaluated by accuracy. Segmentation of histological layers was highly effective (DSC > 0.92), with lower performance for nuclei (DSC = 0.68). Keratinization classification reached 0.92 accuracy: (hyper)orthokeratosis 0.98, hyperparakeratosis 0.93, parakeratosis 0.94. Lesion-level OL/OLL classification achieved 0.929 accuracy, with slightly better effectiveness in transverse sections than tangential sections (0.944 vs. 0.925). The AI system demonstrated strong segmentation and classification capabilities, supporting its potential to enhance diagnostic accuracy, reproducibility, and efficiency for the assessment of OL samples.
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