Image-based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks.

ABSTRACT:

Background

The Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring.

Objectives

To assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.

Methods

Imaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamati

SUBMITTER: Schaap MJ 

PROVIDER: S-EPMC9298301 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature