High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

ABSTRACT:

Background

Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin.

Methods

We analyzed biopsies from two independent (primary and secondary) coho

SUBMITTER: Correia C 

PROVIDER: S-EPMC7071594 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature