Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment

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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