Using advanced technology to better predict heart disease risk
Official title: Opportunistic Screening for ASCVD using a Multimodal Deep Learning Risk Prediction Model
Funded at Mayo Clinic Arizona · NIH-10999423 · Data updated 2026-11-12
This study is working on a new tool to better predict the risk of heart attacks and strokes for people with atherosclerotic cardiovascular disease, using a mix of CT scan images and health information, so that patients can get more personalized advice on how to stay healthy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Mayo Clinic Arizona NIH-funded |
| Lab location | 1 site (Scottsdale, United States) |
| Project start | December 15, 2023 |
| Project end | November 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $615,394 |
| NIH project number | 5R01HL167811-02 |
| Project ID | NIH-10999423 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals over 21 years old who are at risk for atherosclerotic cardiovascular disease.
✕Less likely to help
Patients who are under 21 years old or those without risk factors for heart disease may not benefit from this research.
What this research studies
This research aims to improve the prediction of atherosclerotic cardiovascular disease (ASCVD), which includes serious conditions like heart attacks and strokes. By developing a new model called ADMIRE, the team will combine various types of data, including imaging from CT scans and traditional clinical information, to create a more accurate risk assessment tool. This approach addresses the limitations of current methods, which often fail to account for diverse populations and important biomarkers. Patients may benefit from more personalized prevention strategies based on their unique risk profiles.
Why it matters
Potential benefit: If successful, this research could lead to more accurate risk assessments for heart disease, allowing for better prevention and treatment strategies.
How similar studies have gone: Previous research has shown promise in using multimodal approaches for risk prediction, suggesting that this innovative method could be effective.
Where this research is happening
-
Mayo Clinic Arizona
Scottsdale, United States Active
Researchers
- Principal investigator: Banerjee, Imon — Mayo Clinic Arizona
- Study coordinator: Banerjee, Imon
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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.