Improving how we identify lupus kidney disease
Objective Classification of Lupus Nephritis
This project uses computer technology to help doctors better identify and classify kidney disease in people with lupus.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Houston NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project ID | NIH-11125957 on NIH RePORTER |
What this research studies
Lupus nephritis (LN) is a serious kidney complication for many people with lupus, and current methods for diagnosing it from kidney biopsies can sometimes vary between different doctors. This project aims to create advanced computer programs, known as deep learning, to analyze images from kidney biopsies. The goal is for these computer programs to classify the different types of lupus kidney disease as accurately and consistently as expert kidney doctors. Ultimately, this technology will also work to predict how a patient's kidney disease might progress based on their initial biopsy.
Who could benefit from this research
Good fit: This work is relevant for adults and children who have been diagnosed with lupus kidney disease and have undergone a kidney biopsy.
Not a fit: Patients who have not been diagnosed with lupus or do not have kidney involvement may not directly benefit from this specific research.
Why it matters
Potential benefit: If successful, this could lead to more accurate and consistent diagnoses of lupus kidney disease, potentially improving treatment plans and long-term kidney health for patients.
How similar studies have performed: While traditional methods for classifying lupus kidney disease have shown inconsistencies, this project explores a novel application of computer vision and deep learning to improve diagnostic accuracy.
Where this research is happening
Houston, United States
- University of Houston — Houston, United States (Active)
Researchers
- Principal investigator: Mohan, Chandra — University of Houston
- Study coordinator: Mohan, Chandra
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 below.