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.

我们提出了一种新颖的方法，利用深度神经网络在低剂量放射影像中进行可解释的椎体骨折评估（XVFA），结合了椎体检测和不确定性定位关键点。我们将Genant的半定量标准作为可区分的基于规则的方式来分类椎体骨折等级和形态。与以往的工作不同，XVFA提供了与当前临床方法相关的可解释的分类，以及不确定性估计，同时在具有挑战性的环境中，在椎体级别的敏感度达到93％，AUC完整性达到97％，超过了最先进的方法。此外，我们将模型的不确定性估计与读者间的一致性进行比较，模型的可靠性与人工注释者相当。

可解释性脊椎骨折分析与不确定性估计：基于可微分规则的分类方法