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

这个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液的声音 用于血液透析通路的新创建动静脉内瘘（AVF）中的血流。这一监测工具 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， 实现成功的成熟。本研究的具体目的是：（1）创造世界上第一个 血液透析患者新创建的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AVF可能成熟和AVF不太可能成熟的患者。通过 将这种深度学习算法集成到Eko的移动的和云软件平台中，我们预计这种算法将能够更好地监测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州大学（University of North Carolina）的入组中心招募研究受试者。

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).