PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks

TL;DR

This paper introduces PSO-Net, an interpretable deep learning system that assesses psoriasis severity from images, reducing patient burden and variability compared to traditional scoring methods.

Contribution

The paper presents a novel attention-based neural network architecture for automated psoriasis assessment that enhances interpretability and aligns with clinical scoring standards.

Findings

Achieved inter-class correlation scores of 82.2% and 87.8% with clinician raters.

Developed a novel regression activation map for interpretability.

Reduced assessment variability compared to traditional methods.

Abstract

Psoriasis is a chronic skin condition that requires long-term treatment and monitoring. Although, the Psoriasis Area and Severity Index (PASI) is utilized as a standard measurement to assess psoriasis severity in clinical trials, it has many drawbacks such as (1) patient burden for in-person clinic visits for assessment of psoriasis, (2) time required for investigator scoring and (3) variability of inter- and intra-rater scoring. To address these drawbacks, we propose a novel and interpretable deep learning architecture called PSO-Net, which maps digital images from different anatomical regions to derive attention-based scores. Regional scores are further combined to estimate an absolute PASI score. Moreover, we devise a novel regression activation map for interpretability through ranking attention scores. Using this approach, we achieved inter-class correlation scores of 82.2% [95% CI: 77- 87%] and 87.8% [95% CI: 84-91%] with two different clinician raters, respectively.