Automated frailty score from body shape and lung image for lung transplant candidates
Official title: An Automated Frailty Scoring System for Lung Transplantation Based on Bio-Geo-Composition
Funded at University of Pittsburgh at Pittsburgh · NIH-11346012 · Data updated 2026-11-12
A new computer model will use pre-transplant CT scans to automatically score a candidate’s fitness for a lung transplant, aiming to improve outcomes and donor-lung allocation.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | August 15, 2024 |
| Project end | May 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $434,607 |
| NIH project number | 5R01HL174570-03 |
| Project ID | NIH-11346012 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults being evaluated for lung transplantation who undergo pre-transplant chest CT imaging.
✕Less likely to help
Patients not eligible for transplant or those without pre-transplant CT imaging may not benefit.
What this research studies
This project will extract Bio-Geo-Composition features from pre-transplant CT images to create the Pittsburgh Transplant Fitness Score, a frailty metric that predicts intra- and post-operative outcomes. The team will develop advanced automated algorithms to quantify body tissues, lung characteristics, and thoracic geometry, and validate their association with transplant success. The goal is to provide a objective, image-based tool to help clinicians and patients understand transplant suitability and expected recovery. The work leverages AI to translate imaging data into a practical clinical score that could inform decision-making for candidates and clinicians.
Why it matters
Potential benefit: If successful, patients could receive more accurate assessments of transplant readiness and potentially better post-transplant outcomes through more informed selection and timing.
How similar studies have gone: Previous work using imaging-based frailty and predictive modeling has shown promise in related transplant settings, though this exact Bio-Geo-Composition approach is novel.
Where this research is happening
-
University of Pittsburgh at Pittsburgh
Pittsburgh, United States Active
Researchers
- Principal investigator: Pu, Jiantao — University of Pittsburgh at Pittsburgh
- Study coordinator: Pu, Jiantao
Related research
Other NIH-funded projects on the same topics.
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A new scoring system to evaluate lung transplant candidates
This study is working on a new tool to help doctors better understand how fit someone is for a lung transplant by looking at their chest scans, which could lead to better outcomes for patients like you.
-
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This study looks at how things like aging and body health can impact the recovery and well-being of people getting lung transplants, with the goal of finding ways to improve care and support for these patients.
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-
Body composition and outcomes after lung transplant
A patient-focused look at how body fat and muscle measurements around the time of lung transplant relate to recovery, complications, and quality of life after surgery.
-
Exercise-based frailty program for people awaiting lung transplants
A tailored exercise and lifestyle program is being tested to reduce frailty and improve outcomes for people waiting for a lung transplant.
-
AI-enabled bone aging and frailty markers in osteoporosis using advanced imaging
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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.