Arteriovenous fistula stenosis classification method based on Auxiliary Wave and Transformer

Screening vascular access dysfunction in hemodialysis requires tools that are objective and efficient. Listening for bruits during a physical exam is a subjective examination that can detect stenosis also called vascular narrowing when properly performed. Phonoangiograms (PAGs) which is a mathematical analysis of bruits increase the objectivity and sensitivity and permit quantification of stenosis. In this paper, we proposed Vision Transformer (ViT) for PAGs to automatically classify vascular stenosis. In particular, we added an auxiliary waveform to improve the classification performance. Moreover, we used the method of inter-patient verification to verify the performance of the proposed method. The experimental results show that the total F1 score, recall, and precision of the proposed method are 0.984, 1.000, and 0.969 respectively. We believe the method proposed in this paper has the potential to provide a reference for subsequent research.