ArticleScientific reports2025
Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.
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.
The trial behind it
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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Who cites it
2 citing papers in PubMed.
- Digital twins to accelerate target identification and drug development for immune-mediated disorders.FEBS open bio · 2026Review
- Dietary patterns and psoriasis severity in Thai patients: a machine learning approach for small sample data.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients with psoriasis taken in a clinical setting. A latent w-space vector was identified that allowed the degree of severity of the psoriasis in the generated images to be modified. A second latent w-space vector was identified that allowed the size of the psoriasis plaque to be modified and this was used to show the potential to alleviate bias in the training data. With appropriate training data, such an approach could see a future application in a clinical setting where a patient is able to observe a prediction for the appearance of their skin and associated skin condition under a range of treatments and after different time periods, hence allowing an informed and data-driven decision on optimal treatment to be determined.
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Registered trials
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