  We present a novel method for explainable vertebral fracture assessment
(XVFA) in low-dose radiographs using deep neural networks, incorporating
vertebra detection and keypoint localization with uncertainty estimates. We
incorporate Genant's semi-quantitative criteria as a differentiable rule-based
means of classifying both vertebra fracture grade and morphology. Unlike
previous work, XVFA provides explainable classifications relatable to current
clinical methodology, as well as uncertainty estimations, while at the same
time surpassing state-of-the art methods with a vertebra-level sensitivity of
93% and end-to-end AUC of 97% in a challenging setting. Moreover, we compare
intra-reader agreement with model uncertainty estimates, with model reliability
on par with human annotators.
