Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis

Abstract

Objectives To evaluate whether neural networks can distinguish between seropositive rheumatoid arthritis (RA), seronegative RA and psoriatic arthritis (PsA) based on inflammatory patterns from hand MRI and to test how psoriasis patients with subclinical inflammation fit into such patterns. Methods ResNet neural networks were utilized to compare (i) seropositive RA vs. PsA, (ii) seronegative RA vs. PsA and (iii) seropositive vs. seronegative RA with respect to hand MRI data. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without presentation of demographic and clinical parameters. Additionally, the trained networks were applied to psoriasis patients without clinical arthritis. Results MRI scans from 649 patients (135 seronegative RA, 190 seropositive RA, 177 PsA, 147 psoriasis) were fed into ResNet neural networks. AUROC was 75% for seropositive RA vs. PsA, 74% for seronegative RA vs. PsA and 67% for seropositive vs. seronegative RA. All MRI sequences were relevant for classification, however, when deleting contrast agent-based sequences the loss of performance was only marginal. The addition of demographic and clinical data to the networks did not provide significant improvements for classification. Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that a PsA-like MRI pattern may be present early in the course of psoriatic disease. Conclusion Neural networks can be successfully trained to distinguish MRI inflammation related to seropositive RA, seronegative RA, and PsA.

DeepNAPSI multi-reader nail psoriasis prediction using deep learning

“…classify CT scans for the presence of a disease 7 . Lately, neural networks also were applied to challenges in the rheumatology, such as image-based classification of rheumatic diseases via data from magnetic resonance imaging or computed tomography 8 , 9 .…”

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

“…This is not entirely correct, as subtle differences exist. Numerous studies on data mining techniques in RMDs research have been published from the state-of-the-art cutoff date (i.e., February, 22 th 2021) to the date of this manuscript submission. From studies that pursue to distinguish PsA, seronegative, and seropositive RA patients based on hand MRI using an ANN [167], to studies that examine the validity of ML models in predicting GCA flares after GCs tapering [168]. However, the review presented here addresses the main topics in a detailed way, providing a detailed overview for researchers who want to apply AI in RMDs, regardless of this two-year gap. The classification proposed into six main topics, may not be suitable for capturing subtle differences between articles.…”

“…• Numerous studies on data mining techniques in RMDs research have been published from the state-of-the-art cutoff date (i.e., February, 22 th 2021) to the date of this manuscript submission. From studies that pursue to distinguish PsA, seronegative, and seropositive RA patients based on hand MRI using an ANN [167], to studies that examine the validity of ML models in predicting GCA flares after GCs tapering [168]. However, the review presented here addresses the main topics in a detailed way, providing a detailed overview for researchers who want to apply AI in RMDs, regardless of this two-year gap.…”

Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis

“…Modern imaging techniques such as ultrasound, MRI, positron emission tomography (PET) -computed tomography (CT) and high-resolution peripheral quantitative CT (HR-pQCT) are diagnostic tools with high resolution and accuracy for the assessment of synovio-entheseal damage and are major assets for the current diagnostic approach to psoriatic disease. [37][38][39][40][41] The first results of HIPPOCRATES in this field were promising, showing that these imaging technologies can be further developed [42][43][44] to allow assessment of damage progression in PsA at a new level. The use of molecular approaches for damage characterization in PsA is a growing area of research and one of the main focuses of HIPPOCRATES.…”