2024/07/03 by Victor Wåhlstrand Skärström, Lisa Johansson, Skärström, Victor Wåhlstrand +7 · 1 citation
Engineering · Health Professions · #Artificial Intelligence in Healthcare #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.10 #I.4.8 #Image and Video Processing (eess.IV) #J.3 #Medical Imaging and Analysis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2407.02926
openalex publication_date 2024/07/03 · openalex created_date 2024/07/06 · openalex updated_date 2026/07/28
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.