Neural network has been trained to distinguish seronegative and seropositive RA from PsA and each other, according to a study presented at GRAPPA2022.
NEW YORK — On the basis of MRI images of the hand, a neural network has been trained to distinguish seronegative and seropositive rheumatoid arthritis from psoriatic arthritis as well as from each other, according to a study that was presented at the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis.
The images were evaluated with residual neural networks , which represents a sophisticated form of deep learning to facilitate the flow of information across the network layers as they form to improve accuracy in their ability to distinguish one form of disease from the other. The training was performed on images from the T1 coronal, T2 corona1, T1 coronal fat suppressed with contrast, T1 axial fat suppressed with contrast, and T2 fat suppressed axial sequences.
Subsequent to the classification of the patients with psoriasis, 14 of the 147 have developed PsA so far over a relatively short follow-up. All of these were among those identified as PsA by neural network evaluation of the hand MRIs. All of the MRI sequences were relevant for classification, but contrast did not appear to help with accuracy.
"Of course, if we could create an accurate tool with ultrasound, this would be even more practical," said Simon, recognizing the value of an office tool, but he cautioned that this would be far more challenging.Utility:"In Challenging Cases if the Accuracy Improves?"
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