Dialysis access monitoring using a digital stethoscope-based deep learning system

该SBIR第一阶段项目将开发一种基于深度学习的算法来分析血音 用于血液透析通路的新创建的动静脉瘘(AVF)中的流动。此监控工具 可以帮助识别需要手术干预的患者中不太可能成熟的瘘管 实现成功成熟。这项研究的具体目标是(1)创造世界上第一个深度 来自血液透析患者的新创建的AVF声音的学习规模数据库，以及(2)开发和 评估通过半监督学习训练的深度学习分类模型的性能 区分可能成熟的动静脉瘘患者和不太可能成熟的动静脉瘘患者。通过 将这种深度学习算法集成到EKO的移动和云软件平台中，我们预计该算法将能够更好地监控新创建的瘘管的成熟过程。在项目的第一阶段，我们将在北卡罗来纳大学(UNC)的访问中心招募研究对象。

This SBIR Phase I project will develop a deep learning-based algorithm to analyze the sound of blood flow in newly created arteriovenous fistulas (AVFs) used for hemodialysis access. This monitoring tool can help to identify fistulas that are unlikely to mature in patients who need surgical intervention to achieve successful maturation. The specific aims of the study are (1) to create the world’s first deep learning-scale database of newly created AVF sounds from hemodialysis patients, and (2) develop and evaluate the performance of a deep learning classification model trained via semi-supervised learning to discriminate between patients with AVFs likely to mature and patients with AVFs unlikely to mature. By integrating this deep learning algorithm into Eko’s mobile and cloud software platform, we anticipate this algorithm will enable better monitoring of the maturation process for newly created fistulas. During Phase I of the project, we will recruit study subjects in access centers at the University of North Carolina (UNC).