ArticleScientific reports2025
Artificial intelligence-assisted prediction of Demodex mite density in facial erythema.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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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2 citing papers in PubMed.
- Comparative Evaluation of ChatGPT-5.2, Claude Sonnet 4.5, and DeepSeek-V3.2 for Rosacea-Related Information: Accuracy, Reliability, Readability, and Reference Hallucinations.Diagnostics (Basel, Switzerland) · 2026Article
- NN-assisted image analysis for quantifying intracellularFrontiers in cellular and infection microbiology · 2026Article
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11 authors.
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Abstract
Current detection methods of Demodex mite density in facial erythema are semi-invasive or operator-dependent. We developed and evaluated a deep learning model (DemodexNet) for predicting Demodex mite density and assessed its impact on the diagnostic performance of dermatologists. This study included 1,124 patients with facial erythema who underwent Demodex mite density measurement at two referral hospitals between January 2016 and August 2023. DemodexNet achieved area under the receiver operating characteristic curve values of 0.823-0.865 in internal testing, with lower values observed in the external testing set. AI-assisted evaluation was associated with an increase in diagnostic accuracy among dermatologists from 63.7% to 70.6% (P < .001). Less experienced dermatologists and those with higher trust in AI showed greater performance gains. The model recognized central facial regions and individual lesions characteristic of demodicosis. DemodexNet demonstrates promising performance in predicting Demodex mite density and significantly improves dermatologists' diagnostic accuracy. As this proof-of-concept study was limited to Korean patients with Fitzpatrick skin types III-IV, validation in diverse populations is required before broader clinical application.
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