Opportunistic heart risk detection from abdominal CTs using robust AI
Official title: Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep Learning
Funded at Stanford University · NIH-11381592 · Data updated 2026-11-12
A doctor-scientist team at Stanford will use existing abdominal CT scans and medical records to build a trustworthy AI tool that helps predict future heart problems in adults.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | September 1, 2023 |
| Project end | May 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $566,905 |
| NIH project number | 5R01HL167974-04 |
| Project ID | NIH-11381592 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults undergoing abdominal CT scanning and with available electronic medical records.
✕Less likely to help
People without an abdominal CT or with limited access to medical records may not benefit from this approach.
What this research studies
This project will create automated methods to analyze abdominal CT scans and EMR data to estimate risk of cardiovascular death, heart attack, and stroke in adults. The team will develop segmentation algorithms to measure body fat, muscle, liver, bone, and vascular calcifications, with built-in quality checks. They will test the model in real-world, multi-site data and compare it to current risk scores to see if the AI approach can improve prediction. The study plans to validate the approach externally using large datasets to ensure robustness before clinical use.
Why it matters
Potential benefit: If successful, the approach could improve early identification of people at high ASCVD risk using scans they already receive, enabling timely prevention.
How similar studies have gone: Previous work shows AI can enhance imaging-based risk prediction, but this study aims to improve generalizability with large real-world data.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Chaudhari, Akshay — Stanford University
- Study coordinator: Chaudhari, Akshay
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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.