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

> **NIH NIH R43** · EKO DEVICES, INC. · 2021 · $299,358

## Abstract

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).

## Key facts

- **NIH application ID:** 10255460
- **Project number:** 1R43DK129107-01
- **Recipient organization:** EKO DEVICES, INC.
- **Principal Investigator:** PRABIR ROY-CHAUDHURY
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $299,358
- **Award type:** 1
- **Project period:** 2021-06-15 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10255460

## Citation

> US National Institutes of Health, RePORTER application 10255460, Dialysis access monitoring using a digital stethoscope-based deep learning system (1R43DK129107-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10255460. Licensed CC0.

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