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

TitleMachine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation.
Publication TypeJournal Article
Year of Publication2023
AuthorsMehta B, Goodman S, DiCarlo E, Jannat-Khah D, J Gibbons AB, Otero M, Donlin L, Pannellini T, Robinson WH, Sculco P, Figgie M, Rodriguez J, Kirschmann JM, Thompson J, Slater D, Frezza D, Xu Z, Wang F, Orange DE
JournalArthritis Res Ther
Date Published2023 Mar 02
KeywordsArthritis, Rheumatoid, Humans, Inflammation, Machine Learning, Osteoarthritis, Synovial Membrane

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, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82.

CONCLUSIONS: H&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.

Alternate JournalArthritis Res Ther
PubMed ID36864474
PubMed Central IDPMC9979511
Institute of Artificial Intelligence for Digital Health
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