AI to identify types of lung recovery after COVID-19
Deep Learning and Subtyping of Post-COVID-19 Lung Progression Phenotypes
This project uses artificial intelligence on chest X-rays and CT scans to find different lung recovery patterns in people who had COVID-19.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Iowa NIH-funded |
| Lab location | 1 site (Iowa City, United States) |
| Project ID | NIH-11377317 on NIH RePORTER |
What this research studies
Researchers will train advanced deep-learning models on large collections of chest X-rays and CT scans to learn imaging patterns linked to post-COVID lung changes. They will use contrastive self-supervised learning to take advantage of the wide availability of X-rays and the greater detail in CT images. The team will group survivors into distinct subtypes and link those imaging patterns to clinical information and biological markers. The aim is to clarify typical progression patterns and point to signals that could guide follow-up care.
Who could benefit from this research
Good fit: Adults who previously tested positive for COVID-19 and have had chest imaging or ongoing respiratory symptoms would be the most appropriate participants.
Not a fit: People without a history of COVID-19 or whose lung problems are clearly due to other diagnoses are unlikely to benefit from this specific research.
Why it matters
Potential benefit: If successful, the work could help clinicians personalize monitoring and treatment by recognizing different lung recovery types after COVID-19.
How similar studies have performed: Some prior AI studies have shown promise detecting COVID-related lung damage, but combining X-ray and CT to define post-COVID subtypes with contrastive self-supervised methods is relatively new.
Where this research is happening
Iowa City, United States
- University of Iowa — Iowa City, United States (Active)
Researchers
- Principal investigator: Lin, Ching-Long — University of Iowa
- Study coordinator: Lin, Ching-Long
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.