ArticleJournal of imaging informatics in medicine2026
Portable Deep Learning-Assisted Device for Automated Interincisal Opening Assessment and Oral Submucous Fibrosis Lesion Identification: A Translational Clinical Study.
Article in Journal of imaging informatics in medicine, 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 submucous fibrosis (OSMF) is a chronic, progressive, potentially malignant disorder prevalent in Southeast Asia and the Indian subcontinent. Progressive fibrosis restricts mouth opening, making interincisal opening as a key parameter of disease staging. This study developed a portable AI-based device for automated interincisal opening measurement and OSMF lesion detection important for real-time screening and staging. A portable device powered by a quad-core ARM Cortex-A72 processor integrates a YDLiDAR depth camera for interincisal opening measurement and an intraoral camera for image acquisition. Structured inquiry into temporomandibular joint symptoms and OSMF-associated habit history reduces false-positive classification. The AI pipeline comprises buccal mucosa segmentation, OSMF classification, Stages I-III grading based on interincisal opening, and lesion detection. Model robustness is evaluated using stratified fivefold cross-validation and Grad-CAM analysis. Bland-Altman analysis assesses agreement between LiDAR and vernier-caliper measurements, while quadratic weighted Cohen's kappa evaluates agreement between AI- and clinician-assigned OSMF stages. A total of 350 OSMF (including 10 biopsy-confirmed cases) and 334 non-OSMF images were collected. U-Net with EfficientNet-B2 achieved accurate mucosal segmentation, DenseNet-121 showed the best classification performance (accuracy/F1, 0.991), and U-Net with ResNet-34 enabled lesion localization. Clinical validation in 100 subjects achieved 98.0% sensitivity, 96.0% specificity, and 97.0% accuracy. Bland-Altman analysis confirmed agreement between interincisal and vernier-caliper measurements (SD, 0.68 mm), while AI-based staging showed substantial agreement (weighted kappa k = 0.982, 95% CI 0.956-1.00). The proposed system provides a reliable, cost-effective solution for early OSMF screening and decision support in government healthcare and community settings.
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