FC20 Automating psoriasis area and severity index assessment from real-world images using Vision Transformers (2025)

The psoriasis area and severity index (PASI) is the gold standard for assessing psoriasis severity but is time-consuming, subjective, poorly reproducible, and requires face-to-face interaction between clinician and patient. Automating PASI assessments using deep learning-based image analysis may provide a more objective, efficient measure of disease severity in trial and real-world settings. Most studies have used Convolutional Neural Networks.¹ However, Vision Transformers (ViTs) are sophisticated deep learning models that have shown unprecedented capabilities in image analysis, including classifying skin lesions from dermoscopic images and 3D image (computed tomography/magnetic resonance imaging scan) analysis. We sought to develop and evaluate a ViT model for automated PASI using images. Following consent, adults with chronic plaque psoriasis were recruited via a UK national specialized psoriasis service (04/2021–05/2024). Professional studio photographs (five standardized views) and self-taken photographs (three views) were captured. Two blinded, independent face-to-face physician PASI measures were recorded, with the first rater used as the primary reference value. Fitzpatrick Skin Type and Physician Global Assessment (PGA) were also noted. We trained our ‘MultiViT’ model over 300 epochs and tested using an 80:20 split of the participants. MultiViT advances beyond standard ViTs by handling multiple images and resolutions simultaneously. Mean absolute error (MAE) was used to assess prediction accuracy and compare the standard ViT models with the MultiViT. We captured 1109 images from 152 participants; 63% were male, median age of 45.5 years [interquartile range (IQR) 33.75–57.0], median body mass index of 29.68, 84% Fitzpatrick Skin Types I–IV, and 16% V and VI. The majority (76%) had mild-to-moderate disease (PGA ≤3); median PASI was 8.2 (IQR 5.7–12.7). The MultiViT model demonstrated an MAE of 3.88 for predicting PASI from skin images vs. 5.13 for the standard ViT. When stratified by skin type, I–IV performed better than V and VI (MAE of 2.80 and 8.17, respectively), and by disease severity, PGA ≤3 performed better than PGA >3 (MAE of 1.97 and 9.13, respectively). Our findings suggest the MultiViT model offers potential for automated psoriasis severity assessments from skin images, with favourable prediction accuracy despite a small dataset. An expanded and more diverse (Fitzpatrick Skin Type, severity) dataset will enable further refinement, validation and testing of the MultiViT model in trial and real-world settings. Future integration into a smartphone application will facilitate self-monitoring of psoriasis and healthcare efficiency for improved health and cost outcomes in psoriasis.