PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks
This addresses the need for automated, less burdensome monitoring of psoriasis patients, reducing variability and time in clinical assessments.
The paper tackled the problem of automating psoriasis severity assessment by developing PSO-Net, an interpretable deep learning system that estimates Psoriasis Area and Severity Index (PASI) scores from digital images, achieving inter-class correlation scores of 82.2% and 87.8% with clinician raters.
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.