Dialysis access monitoring using a digital stethoscope-based deep learning system - this sbir phase I project will develop a deep learning-based algorithm to analyze the sound of blood flow in newly…

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