Diagnosis of sacroiliitis using MR images with a simplified custom deep learning model

Abstract Axial spondyloarthritis (SpA) is an inflammatory disease that causes back pain by affecting the axial skeleton, and the sacroiliac (SI) joints are mostly involved. An early diagnosis is required to prevent structural damage. Magnetic resonance imaging (MRI) is the primary tool for the diagnosis of axial spondyloarthritis; however, the occurrence of noninflammatory degenerative changes might prevent the accurate diagnosis even for experts. Deep learning (DL) aims to assist radiologists in detecting and diagnosing sacroiliitis by providing distinct and effective feature extraction along MR sequences. This retrospective study considers a primary dataset with 50 clinical sacroiliitis and 50 control group patients and aims to diagnose sacroiliitis using 4 MRI sequences. For this purpose, a simplified convolutional neural network model is developed, and comprehensive comparative experiments and analyses are performed. Three pre-trained DL models are considered in a comparative study using a transfer learning approach. Image-based, sequence-based, and patient-based experiments are conducted to evaluate general diagnostic abilities, analyze further clinical implementations, and determine the most informative and challenging MRI sequences. The results showed that the proposed model has the ability to detect sacroiliitis with 0.951 and 0.977 accuracy in patient and image-based experiments, respectively. The deep learning models obtained promising results for future clinical implementation to assist radiologists in detecting sacroiliitis. It is also analyzed that the STIR coronal images are the most challenging, while T1 axial sequences are the most informative sequences for sacroiliitis diagnosis.