AI-powered home sensor system to monitor kidney health after acute kidney injury
SCH: Artificial Intelligence enabled multi-modal sensor platform for at-home health monitoring of patients
This project is building an easy home kit with urine dipsticks, a small ECG patch, and a phone app to help adults who had acute kidney injury monitor their health and get alerts if they may need follow-up care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Arizona State University-Tempe Campus NIH-funded |
| Lab location | 1 site (Scottsdale, United States) |
| Project ID | NIH-11137733 on NIH RePORTER |
What this research studies
After an acute kidney injury, you would use a color-changing urine dipstick at home to capture markers like creatinine and urea, and wear a thin battery-less ECG patch that reads your heart rhythm. The patch and dipstick data are sent to a smartphone app that also includes your medical history and health information. An on-phone AI model combines the urine and ECG results with your past health data to estimate your risk of AKI coming back and can prompt you to seek nephrology care. The system is designed for automatic, at-home monitoring to reduce travel and make follow-up easier and more timely.
Who could benefit from this research
Good fit: Adults aged 21 and older who recently experienced acute kidney injury and can use a smartphone, perform simple urine tests at home, and wear a small ECG patch are ideal candidates.
Not a fit: People without a history of AKI, those who cannot use a smartphone or wearable devices, or patients with conditions that prevent reliable urine or ECG sampling may not benefit from this approach.
Why it matters
Potential benefit: If successful, this could catch signs of worsening kidney health earlier, reduce hospital readmissions, and make specialist follow-up easier for AKI survivors.
How similar studies have performed: Home urine testing and wearable ECGs have been used separately in other health programs, but combining these sensors with on-device AI to predict AKI recurrence is a relatively new and unproven approach.
Where this research is happening
Scottsdale, United States
- Arizona State University-Tempe Campus — Scottsdale, United States (Active)
Researchers
- Principal investigator: Sanyal, Arindam — Arizona State University-Tempe Campus
- Study coordinator: Sanyal, Arindam
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 below.