Predicting Post-Covid Lung Scarring with Easy-to-Understand Deep Learning and Key Biomarkers
Official title: Predicting Post-Covid Pulmonary Fibrosis with Explainable Deep Learning and Optimal Biomarker Discovery
Funded at Northwestern University · NIH-11380916 · Data updated 2026-11-12
Aims to use explainable artificial intelligence and biomarker data from multiple hospitals to predict which patients will develop long-term lung scarring after COVID-19.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northwestern University NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project start | September 1, 2024 |
| Project end | May 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $693,398 |
| NIH project number | 5R01HL171376-03 |
| Project ID | NIH-11380916 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients recently or previously infected with COVID-19 who have imaging and health data available from participating centers.
✕Less likely to help
Patients without access to comprehensive imaging/EHR data or those with low risk of fibrosis may not benefit.
What this research studies
This project uses multimodal data, combining chest imaging and electronic health records, to forecast who is at risk for post-COVID pulmonary fibrosis. It will develop transparent deep learning methods so clinicians can understand how predictions are made. Data will come from several centers, focusing on early identification to enable timely treatment with available options. The goal is to guide early interventions and potentially improve long-term lung outcomes for patients recovering from COVID-19.
Why it matters
Potential benefit: If successful, patients at high risk could get earlier therapies to prevent or mitigate lung fibrosis and improve recovery.
How similar studies have gone: Other studies have explored AI-based risk prediction for post-COVID complications, with some promising early results but this approach emphasizes explainability and multimodal data.
Where this research is happening
-
Northwestern University
Chicago, United States Active
Researchers
- Principal investigator: Bagci, Ulas — Northwestern University
- Study coordinator: Bagci, Ulas
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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.