Psoriasis Severity Assessment with a Similarity-Clustering Machine Learning Approach Reduces Intra- and Inter-observation variation

TL;DR

This study introduces a similarity-clustering machine learning approach using digital images to assess psoriasis severity, significantly reducing observer variation and improving consistency over traditional methods.

Contribution

The paper presents a novel digital image-based similarity clustering method that enhances psoriasis severity assessment accuracy and consistency among dermatologists.

Findings

Repeated similarity clustering achieved over 95% consistent ratings.

Pearson correlation of 0.72 between absolute and pairwise scoring.

Digital image approach reduces intra- and inter-observer variation.

Abstract

Psoriasis is a complex disease with many variations in genotype and phenotype. General advancements in medicine has further complicated both assessments and treatment for both physicians and dermatologist alike. Even with all of our technological progress we still primarily use the assessment tool Psoriasis Area and Severity Index (PASI) for severity assessments which was developed in the 1970s. In this study we evaluate a method involving digital images, a comparison web application and similarity clustering, developed to improve the assessment tool in terms of intra- and inter-observer variation. Images of patients was collected from a mobile device. Images were captured of the same lesion area taken approximately 1 week apart. Five dermatologists evaluated the severity of psoriasis by modified-PASI, absolute scoring and a relative pairwise PASI scoring using similarity-clustering and conducted using a web-program displaying two images at a time. mPASI scoring of single photos by the same or different dermatologist showed mPASI ratings of 50% to 80%, respectively. Repeated mPASI comparison using similarity clustering showed consistent mPASI ratings > 95%. Pearson correlation between absolute scoring and pairwise scoring progression was 0.72.