Using AI to detect narrowing in dialysis patients' blood vessels through sound recordings
Official title: Optimization and Validation of an AI Model that Screens for Arteriovenous Fistula Stenosis in Dialysis Patients using Sound Files from a Digital Stethoscope
Funded at Weill Medical Coll of Cornell Univ · NIH-11132384 · Data updated 2026-11-12
This study is testing a new AI tool that listens to sounds from dialysis patients' arteriovenous fistulas to see if it can spot problems like stenosis, and it aims to find out if this tool can be used just as well by trained technicians and patients as the current standard method.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | June 1, 2025 |
| Project end | May 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $503,966 |
| NIH project number | 1R01EB036037-01A1 |
| Project ID | NIH-11132384 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are dialysis patients who have arteriovenous fistulas for vascular access.
✕Less likely to help
Patients who do not have arteriovenous fistulas or are not undergoing dialysis may not benefit from this research.
What this research studies
This research aims to develop and validate an artificial intelligence model that can screen for stenosis in arteriovenous fistulas (AVF) used by dialysis patients. By analyzing sound files collected from a digital stethoscope, the AI model will be trained on a large dataset and its performance will be compared to traditional angiography, which is the current gold standard for diagnosis. The study will also explore how accurately trained technicians and patients can use this technology for screening. This innovative approach seeks to improve the management of vascular access, which is crucial for the health of dialysis patients.
Why it matters
Potential benefit: If successful, this research could lead to earlier detection of vascular issues in dialysis patients, potentially reducing complications and improving patient outcomes.
How similar studies have gone: Other studies have shown promise in using AI for medical diagnostics, suggesting that this approach could be effective, although this specific application is relatively novel.
Where this research is happening
-
Weill Medical Coll of Cornell Univ
New York, United States Active
Researchers
- Principal investigator: Mosadegh, Bobak — Weill Medical Coll of Cornell Univ
- Study coordinator: Mosadegh, Bobak
Related research
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AI tool to screen AV fistula stenosis using stethoscope sounds in dialysis patients
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.