Lumbar facet arthropathy is frequently observed along with other degenerative changes of the spine in patients presenting with chronic low back pain. Deep learning has demonstrated unprecedented success in automated assessment of many spine degenerative changes, but heretofore not applied to facet arthropathy. This study presents binary classification of facet arthropathy (normal/mild vs moderate/severe) on T2-weighted axial MRI slices using a two-staged approach: zero-shot facet detection followed by classification. Our model achieves an AUC of 0.916 [0.911, 0.921] with sensitivity and specificity of 97.8% [97.4, 98.3] and 64.1% [63.1, 65.1], respectively and can potentially enhance the clinical workflow.
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