Developing advanced methods to improve diagnosis of autoimmune diseases
Official title: Unified and fair multimodal representation learning for autoimmune diseases
Funded at Univ of North Carolina Chapel Hill · NIH-11091113 · Data updated 2026-11-12
This study is working on a new way to help doctors diagnose autoimmune diseases like lupus more accurately and quickly, especially for Black and Hispanic women who are often affected more, by using different types of health information together.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | September 16, 2024 |
| Project end | September 15, 2026 |
| Funding year | 2024 |
| Award (this year) | $1,972,080 |
| NIH project number | 1OT2OD038045-01 |
| Project ID | NIH-11091113 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include women, particularly those who are Black or Hispanic, who are experiencing symptoms of autoimmune diseases like lupus.
✕Less likely to help
Patients with autoimmune diseases who do not fit the demographic focus or those who have already received a timely diagnosis may not benefit from this research.
What this research studies
This research aims to enhance the diagnosis of autoimmune diseases, particularly systemic lupus erythematosus, by creating a sophisticated multimodal representation learning technology. It will integrate various data types, including electronic health records, imaging, and clinical measures, to provide more accurate and timely diagnoses. The approach focuses on addressing health equity concerns, especially for Black and Hispanic women who are disproportionately affected. By utilizing advanced machine learning techniques, the project seeks to deliver personalized disease predictions and improve the overall diagnostic process.
Why it matters
Potential benefit: If successful, this research could significantly reduce the time to diagnosis for autoimmune diseases, leading to better patient outcomes and reduced risk of severe complications.
How similar studies have gone: Previous research has shown promise in using multimodal machine learning approaches for improving diagnostic accuracy in various medical fields, suggesting potential success for this novel application.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Sheikh, Saira Z — Univ of North Carolina Chapel Hill
- Study coordinator: Sheikh, Saira Z
Related research
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Unified and fair multimodal learning for autoimmune diseases
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Researchers are studying how B cells and their epigenetic marks behave in people with autoimmune diseases like SLE to find new ways to treat these conditions.
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.