Machine Learning Pipeline for Predicting Bone Marrow Edema Along the Sacroiliac Joints on Magnetic Resonance Imaging

Abstract

ObjectiveTo develop and validate a fully automated machine learning (ML) algorithm that predicts bone marrow oedema (BMO) on a quadrant‐level in sacroiliac (SI) joint MRI.MethodsA computer vision workflow automatically locates the SI joints, segments regions of interest (ilium and sacrum), performs objective quadrant extraction and predicts presence of BMO, suggestive of inflammatory lesions, on a quadrant‐level in semi‐coronal slices of T1/T2‐weighted MRI scans. Ground truth was determined by consensus among human readers. The inflammation classifier was trained using a ResNet18 backbone and 5‐fold cross‐validated on scans of spondyloarthritis (SpA) patients (n=279), postpartum (n=71), and healthy subjects (n=114); while independent SpA patient MRIs (n=243) served as test dataset. Patient‐level predictions were derived from aggregating quadrant‐level predictions, i.e. at least one positive quadrant.ResultsThe algorithm automatically detects the SI joints with a precision of 98.4% and segments ilium/sacrum with an intersection‐over‐union of 85.6% and 67.9%, respectively. The inflammation classifier performed well in cross‐validation: area under the curve (AUC) 94.5%, balanced accuracy (B‐ACC) 80.5%, and F1 score 64.1%. In the test dataset, AUC was 88.2%, B‐ACC 72.1%, and F1 score 50.8%. On a patient‐level, the model achieved a B‐ACC of 81.6% and 81.4% in the cross‐validation and test dataset, respectively.ConclusionWe propose a fully automated ML pipeline that enables objective and standardized evaluation of BMO along the SI joints on MRI. This method has the potential to screen large numbers of (suspected) SpA patients and is a step closer towards artificial intelligence assisted diagnosis and follow‐up.