Fully automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis – Feasibility study

In the present paper we propose an automated procedure for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis (axSpA). The procedure is based on MRI examination of the sacroiliac joints of 30 patients with confirmed sacroiliitis in the course of axSpA (18 of patients were male, while 12 of patients were female; the mean age of patients was 28.8 ± 9.0 years, range from 18 to 45 years). The segmentation of the sacral and iliac bones is performed using U-Net-like architecture. The subchondral bone regions are found, where inflammatory changes are expected to appear. Convolutional classification architectures are trained to classify image voxels as either being within normal or inflammatory-changed areas. The deep learning-based classification of voxels is compared to a method based on statistical testing. The Dice coefficient for segmentation of subchondral bone was 0.84 (standard deviation 0.06). The sensitivity of the detection of inflammatory changes was 0.88. The specificity of the detection of inflammatory changes was 0.91. The discrepancy between sensitivity and specificity values achieved by the automated method and the human readers is attributed to ‘‘a satisfaction of search” phenomenon. After verification of the automated detections by human readers sensitivity and specificity increased to 0.95 and 0.96, respectively. The Spearman’s correlation coefficient between the volumes of lesions calculated manually and automatically is equal to 0.866 while the intraclass coefficient of correlation ICC(1,1) is equal to 0.947. The study demonstrates that an automated detection of inflammatory lesions with high precision of lesion volume assessment is feasible.

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Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).