Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation.

ABSTRACT:

Background

We sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples.

Methods

We compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs.

Results

Synovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binu

SUBMITTER: Mehta B 

PROVIDER: S-EPMC9979511 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature