ArticleFrontiers in medicine2026
Objective assessment of rosacea erythema severity: a multimodal artificial intelligence framework integrating VISIA® imaging and image-derived tabular features.
Article in Frontiers 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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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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6 authors.
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Abstract
Rosacea is a chronic inflammatory skin disorder characterized by persistent facial erythema. Its clinical assessment relies on the Clinician's Erythema Assessment (CEA), a subjective scale prone to inter-observer variability. To address the need for diagnostic consistency, this study developed a multimodal artificial intelligence framework for objective CEA grading using standardized VISIA® imaging. We analyzed a retrospective cohort of 1,001 patients. To establish a robust reference standard, three expert dermatologists conducted a multi-step collective audit to reach a unanimous consensus for each case. The framework integrated handcrafted image-derived tabular features with deep learning representations. During training, spatial data augmentations and Focal Loss were implemented to address dataset imbalance and mitigate overfitting. Our results demonstrated that the multimodal fusion model achieved statistically significant improvements over the strong image-only baseline (McNemar's
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