Using advanced imaging and algorithms to improve healthcare decision-making
Official title: Information-Theoretic Surprise-Driven Approach to Enhance Decision Making in Healthcare
Funded at Arizona State University-Tempe Campus · NIH-10575550 · Data updated 2026-11-12
This study is looking at how using advanced imaging and smart computer techniques can help doctors better identify patients who might have genetic risks for conditions like breast cancer and Alzheimer's, making it easier to catch these issues early and accurately.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Arizona State University-Tempe Campus NIH-funded |
| Lab location | 1 site (Tempe, United States) |
| Project start | August 1, 2023 |
| Project end | July 31, 2026 |
| Funding year | 2023 |
| Award (this year) | $554,471 |
| NIH project number | 1R21EB033923-01 |
| Project ID | NIH-10575550 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for breast cancer, Alzheimer's disease, or coronary heart disease who may benefit from enhanced diagnostic testing.
✕Less likely to help
Patients who do not have risk factors for the targeted conditions or those who have already been accurately diagnosed may not receive benefit from this research.
What this research studies
This research investigates how combining traditional clinical guidelines with advanced imaging techniques and machine learning algorithms can enhance the accuracy of patient stratification for diagnostic testing. Specifically, it aims to improve the identification of patients who may carry genetic mutations linked to conditions like breast cancer and Alzheimer's disease. By analyzing imaging phenotypes alongside existing guidelines, the research seeks to provide a more reliable method for screening patients, potentially leading to earlier and more accurate diagnoses. The study will also explore the causal relationships between these health conditions and imaging data to better integrate these findings into clinical practice.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnostic testing and earlier detection of serious health conditions for patients.
How similar studies have gone: Previous research has shown promise in using machine learning and imaging techniques to improve diagnostic accuracy, indicating that this approach could be effective.
Where this research is happening
-
Arizona State University-Tempe Campus
Tempe, United States Active
Researchers
- Principal investigator: Iquebal, Ashif — Arizona State University-Tempe Campus
- Study coordinator: Iquebal, Ashif
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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.