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.

翻译：我们提出了一种新颖的可解释性椎体骨折评估（XVFA）方法，用于低剂量X光片，该方法采用深度神经网络，并结合了椎体检测、关键点定位以及不确定性估计。我们将Genant半定量标准作为一种可微的、基于规则的分类手段，用于判定椎体骨折等级与形态。与先前工作不同，XVFA不仅提供了与当前临床方法相关联的可解释分类以及不确定性估计，同时在极具挑战性的场景下超越了现有最优方法，实现了椎体级别93%的敏感性和端到端97%的AUC。此外，我们将模型的不确定性估计与阅片者内部一致性进行了比较，结果表明模型的可靠性达到了与人类标注者相当的水平。