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Objective: To investigate the association of spinal inflammation on MRI in patients with various clinical, functional and radiological outcomes in patients with axial spondyloarthritis (SpA)., Methods: Three hundred and ninety-seven participants with axial SpA and back pain were recruited from 10 rheumatology centres. Clinical, biochemical and radiological parameters were collected and participants underwent MRI of the spine. MRI features including inflammatory lesions of facet joints and costovertebral joints, corner inflammatory lesions, and spondylitis were assessed. BASFI, Bath Ankylosing Spondylitis Disease Activity Index, Bath Ankylosing Spondylitis Global Index, BASMI and modified Stoke Ankylosing Spondylitis Spinal Score were measured. Multivariate linear regression models were used to determine the associations between MRI parameters and various clinical, functional and radiological outcomes., Results: BASMI and BASFI correlated well with inflammatory features in spinal MRI. Multivariate analysis showed that lumbar facet joint inflammation was independently associated with BASMI (regression coefficient (β) = 0.12, P < 0.001), lumbar spinal flexion (β = 0.13, P = 0.00), lateral spinal flexion (β = 0.09, P = 0.04), tragus-to-wall distance (β = 0.16, P < 0.001) and BASFI (β = 0.14, P = 0.01). Costovertebral joint inflammation was also associated with BASMI (β = 0.08, P = 0.05)., Conclusion: Inflammatory lesions of facet and costovertebral joints in MRI are associated with restriction in spinal mobility and functional impairment. These important yet commonly overlooked lesions should be reviewed in clinical practice in patients with SpA., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective The aim of this study was to develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI. Methods A total of 326 participants with axial SpA, and 63 participants with non-specific back pain (NSBP) were recruited. STIR MRI of the SI joints was performed and clinical data were collected. Region of interests (ROIs) were drawn outlining bone marrow oedema, a reliable marker of active inflammation, which formed the ground truth masks from which 'fake-colour' images were derived. Both the original and fake-colour images were randomly allocated into either the training and validation dataset or the testing dataset. Attention U-net was used for the development of deep learning algorithms. As a comparison, an independent radiologist and rheumatologist, blinded to the ground truth masks, were tasked with identifying bone marrow oedema in the MRI scans. Results Inflammatory sacroiliitis was identified in 1398 MR images from 228 participants. No inflammation was found in 3944 MRI scans from 161 participants. The mean sensitivity of the algorithms derived from the original dataset and fake-colour image dataset were 0.86 (0.02) and 0.90 (0.01), respectively. The mean specificity of the algorithms derived from the original and the fake-colour image datasets were 0.92 (0.02) and 0.93 (0.01), respectively. The mean testing dice coefficients were 0.48 (0.27) for the original dataset and 0.51 (0.25) for the fake-colour image dataset. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the algorithms using the original dataset and the fake-colour image dataset were 0.92 and 0.96, respectively. The sensitivity and specificity of the algorithms were comparable with the interpretation by a radiologist, but outperformed that of the rheumatologist. Conclusion An MRI deep learning algorithm was developed for detection of inflammatory sacroiliitis in axial SpA. [ABSTRACT FROM AUTHOR]

Objective: Using diffusion‐weighted imaging (DWI)‐derived apparent diffusion coefficient (ADC), we aimed to determine the relationship between intensity of spinal inflammation and mobility in patients with axial spondyloarthritis (SpA) in early and later stages of active disease. The Ankylosing Spondylitis Disease Activity Score (ASDAS) was also used for a more comprehensive evaluation. Methods: Participants with axial SpA and back pain were recruited from 10 rheumatology centers. Clinical, biochemical and radiological parameters were collected. Short tau inversion recovery (STIR) sequence magnetic resonance imaging (MRI) and DWI of the spine and sacroiliac (SI) joints were performed. ADC maps were generated. Participants were examined for Bath Ankylosing Spondylitis Metrology Index (BASMI). Linear regression models were used to determine associations between BASMI and various clinical, radiological, and MRI parameters in participants with active inflammation on spinal ADC maps. Results: One‐hundred and twenty‐seven participants were included in the analyses. Multivariate linear regression showed that mean ADC spine (ß =.16; P =.03), ASDAS‐C‐reactive protein (CRP) (ß =.29, P <.001), and ASDAS‐erythrocyte sedimentation rate (ESR) (ß =.25, P <.01) were associated with BASMI. In participants with duration of back pain ≤3 years, mean spine ADC (ß =.37; P =.03), ASDAS‐CRP (ß =.44; P =.01), and ASDAS‐ESR (ß =.42; P =.01) were associated with BASMI after adjustment for confounding factors. In participants with duration of back pain >3 years, only ASDAS‐CRP (ß =.25; P <.01) and ASDAS‐ESR (ß =.20; P =.20) were associated with BASMI. Conclusion: Intensity of inflammation and clinical disease activity were independently associated with impairment of spinal mobility. The associations were stronger in early (≤3 years) than later disease. [ABSTRACT FROM AUTHOR]

The authors offer a response to a commentary made on their article "Deep Learning Algorithms for Magnetic Resonance Imaging of Inflammatory Sacroiliitis in Axial Spondyloarthritis." They talk about the common solutions applied in their study to reduce overfitting, the 10-fold cross-validation data of their deep learning algorithm, and the training parameters for their algorithms for a comprehensive magnetic resonance imaging (MRI) assessment of axial SpA.