Improving lung cancer diagnosis with advanced bronchoscopy techniques
Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis
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 ID | NIH-10914072 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals who are at risk for lung cancer and may require bronchoscopy for diagnosis.
Not a fit: Patients who have already been diagnosed with lung cancer or those who do not require bronchoscopy for diagnosis may not benefit from this research.
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 performed: 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
Chapel Hill, United States
- 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
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.