Improving lung cancer diagnosis with advanced bronchoscopy techniques
Official title: Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis
Funded at Univ of North Carolina Chapel Hill · NIH-10914072 · Data updated 2026-11-12
This study is looking at how to make bronchoscopy, a test for lung cancer, more accurate by using smart computer technology to help doctors find lung problems better, which could lead to earlier and more effective treatment for patients like you.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | September 1, 2021 |
| Project end | May 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $53,788 |
| NIH project number | 5F30CA265234-04 |
| Project ID | NIH-10914072 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who are at risk for lung cancer and may require bronchoscopy for diagnosis.
✕Less likely to help
Patients who have already been diagnosed with lung cancer or those who do not require bronchoscopy for diagnosis may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of bronchoscopy, a procedure used to diagnose lung cancer, by utilizing machine learning algorithms for real-time localization of the bronchoscope. The goal is to increase the diagnostic yield, which currently stands at only 50-60%, by developing a data-driven model that adapts to individual anatomical differences. By leveraging existing bronchoscope technology without the need for expensive equipment, this approach aims to make lung cancer diagnosis more effective and accessible. Patients undergoing bronchoscopy may benefit from improved detection rates of lung lesions, leading to earlier diagnosis and better treatment outcomes.
Why it matters
Potential benefit: If successful, this research could significantly increase the early detection rates of lung cancer, improving survival outcomes for patients.
How similar studies have gone: Previous research has shown promise in improving diagnostic techniques for lung cancer, but this specific approach using machine learning for bronchoscope localization is relatively novel.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Fried, Inbar — Univ of North Carolina Chapel Hill
- Study coordinator: Fried, Inbar
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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.