Robust Multimodal AI for Breast Cancer Screening
Official title: SCH: Robust Multimodal Longitudinal AI for Enhanced Breast Cancer Screening
Funded at University of Connecticut Storrs · NIH-11389446 · Data updated 2026-11-12
Aiming to improve breast cancer screening with advanced AI that analyzes 3D mammograms to tailor and speed up accurate results for adult women.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Connecticut Storrs NIH-funded |
| Lab location | 1 site (Storrs-Mansfield, United States) |
| Project start | August 1, 2024 |
| Project end | July 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $78,437 |
| NIH project number | 5R01CA297855-03 |
| Project ID | NIH-11389446 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adult women eligible for routine breast cancer screening, particularly those undergoing 3D mammography (DBT) in clinical care.
✕Less likely to help
Women not undergoing regular mammography or those with cancers already diagnosed may see limited direct benefit.
What this research studies
This grant focuses on developing powerful AI tools that work with 3D mammography to personalize breast cancer screening. By using multiple data types and longitudinal information, the project seeks to support radiologists in making faster, more accurate decisions while reducing false alarms. The work envisions real-world use in clinical settings, with emphasis on generalizability across diverse patient populations. If successful, it could lead to earlier detection and less unnecessary follow-up for patients undergoing routine screening.
Why it matters
Potential benefit: Patients could experience earlier and more reliable detection of breast cancer with fewer false positives and less anxiety from unnecessary tests.
How similar studies have gone: Previous AI-CAD approaches reduced radiologist workload but often had high false-positive rates; this study aims to overcome those limitations with longitudinal, multimodal data.
Where this research is happening
-
University of Connecticut Storrs
Storrs-Mansfield, United States Active
Researchers
- Principal investigator: Nabavi, Sheida — University of Connecticut Storrs
- Study coordinator: Nabavi, Sheida
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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.