Abstract 7 — Deep Learning Differentiation of Inflammatory Lesions in Sacroiliac Joint MRI Based on Spondyloarthritis Research Consortium of Canada (SPARCC) System

Abstract

Objective To develop a deep learning algorithm for grading sacroiliitis based on SPARCC in magnetic resonance imaging (MRI). Method A total of 996 images with inflammatory lesions from 210 participants with MRI sacroiliitis were used for training and validation. The testing cohort consisted of 18 participants with and 19 without MRI sacroiliitis. One hundred and fifty four images from the testing cohort had inflammatory lesions identified by a pre-trained algorithm from our previous study[1]. The ground truth was defined by manually outlined regions of interests (ROIs) consisting of bone marrow edema (BME) at the sacroiliac joint. The performance of the deep learning pipeline in predicting the SPARCC score was compared to manual interpretation by two experienced readers. Result The intra-observer reliability and the Pearson coefficient between the SPARCC scores from two experienced readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivities in identifying all inflammatory lesions, deep lesions, and intense lesions were 0.83, 0.79 and 0.81, respectively. The Dice coefficients of the sacrum and ilium segmentation were 0.82 and 0.80, respectively. The accuracies of identifying the SI joint and reference vessel were 0.90 and 0.88, respectively. Conclusion The performance of AI algorithms in SPARCC scoring was compatible with manual scoring by experienced readers. This proposed deep learning pipeline could be the first demonstration of a complete and SPARCC-informed deep-learning approach in scoring STIR images in SpA.