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Artificial intelligence

AI differentiates 2 types of autoimmune arthritis on CT

Computer scientists, rheumatologists and immunologists have pooled skill sets to develop an artificial neural network that can distinguish arthritis type on CT scans of the hand—rheumatoid vs. psoriatic—while also recognizing healthy joints with no arthritis at all.

The team’s report is posted online in Frontiers in Medicine: Rheumatology.

First author Lukas Folle, last author Arnd Kleyer and colleagues at Friedrich-Alexander University of Erlangen in Germany describe their work training and testing the model on 932 scans from 617 patients who were imaged with high-resolution peripheral quantitative CT (HR-pQCT).

The network proved best at using bone shape to identify healthy controls (82% accuracy), followed by rheumatoid arthritis (75%) and psoriatic arthritis (68%). Further, when fed images of joints with undifferentiated arthritis, the AI ​​classified 86% as rheumatoid arthritis, 11% as psoriatic arthritis and 3% as healthy.

Commenting on this latter finding, the authors state that neural networks fed with less well-defined conditions such as undifferentiated arthritis could “allow assigning and clustering such conditions, which in the future and with ongoing refinement of networks could improve disease classification, ie, in the absence of classical biomarkers.”

Folle et al. acknowledge as a limitation their use of HR-pQCT, as it’s not widely used outside of research settings.

In addition, the study excluded osteoarthritis, as the focus was on autoimmune arthritis only.

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