AI-powered tool to better predict and manage acute pulmonary embolism
Official title: AI-driven framework for accurate diagnosis, prognosis, and management of acute pulmonary embolism
Funded at University of Colorado Denver · NIH-11452851 · Data updated 2026-11-12
A new AI system uses imaging and patient data to improve how doctors predict short-term risk and tailor treatment for people with acute pulmonary embolism.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Colorado Denver NIH-funded |
| Lab location | 1 site (Aurora, UNITED STATES) |
| Project start | August 7, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $803,081 |
| NIH project number | 1R01HL188381-01 |
| Project ID | NIH-11452851 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults diagnosed with acute pulmonary embolism treated in emergency or hospital settings who can undergo imaging and biomarker testing and have available prior health data.
✕Less likely to help
Patients with PE that resolve rapidly or have clear, low-risk profiles may see limited incremental benefit from advanced AI risk stratification.
What this research studies
This project develops an AI framework that links imaging findings, patient health conditions, and other clinical data to create outcome-based risk groups for acute PE. It introduces a CTPA-based vision model that recalibrates risk tiers to reflect actual outcomes over 7 and 30 days, then adds multi-modal data (echocardiography, biomarkers, and clinical variables) to refine predictions. An AI recommendation layer will suggest treatment intensity aligned with each patient’s risk and profile, with retrospective analyses planned to test performance against current guidelines.
Why it matters
Potential benefit: If successful, clinicians could more accurately identify which patients need aggressive treatment and which can be managed more conservatively, potentially reducing complications and improving outcomes.
How similar studies have gone: Prior risk-stratification approaches improved calibration but this project aims to tie risk categories directly to realized outcomes with multimodal data, a potentially more accurate method.
Where this research is happening
-
University of Colorado Denver
Aurora, United States Active
Researchers
- Principal investigator: Bai, Harrison — University of Colorado Denver
- Study coordinator: Bai, Harrison
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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.