Improving the detection of missed lung cancer nodules using machine learning.
Increasing Nodule Detection in Lung Cancer by Non-Conscious Detection of "Missed" Nodules and Machine Learning
This study is looking for better ways to help doctors spot tiny lung nodules that could be early signs of lung cancer, using special eye-tracking technology to learn how radiologists notice these nodules, so we can create smart computer programs that help catch what might be missed.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of Massachusetts Med Sch Worcester NIH-funded |
| Lab location | 1 site (Worcester, United States) |
| Project ID | NIH-11036316 on NIH RePORTER |
What this research studies
This research aims to enhance the detection of small lung nodules that may indicate early-stage lung cancer, which is often missed by radiologists. By utilizing eye-tracking technology, the study will identify non-conscious processes that contribute to nodule detection, allowing for the development of machine learning algorithms that can recognize these 'missed' nodules. The approach focuses on training the machine learning system to understand the radiologist's detection patterns rather than just analyzing the images themselves. This innovative method seeks to reduce diagnostic errors and improve early detection rates for lung cancer.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals at high risk for lung cancer, particularly those who may have small nodules detected on CT scans.
Not a fit: Patients with advanced lung cancer or those who do not undergo CT imaging may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could significantly increase the early detection of lung cancer, potentially improving survival rates for patients.
How similar studies have performed: Other research has shown promise in using machine learning and eye-tracking for improving diagnostic accuracy, suggesting that this approach could be effective.
Where this research is happening
Worcester, United States
- Univ of Massachusetts Med Sch Worcester — Worcester, United States (Active)
Researchers
- Principal investigator: Digirolamo, Gregory James — Univ of Massachusetts Med Sch Worcester
- Study coordinator: Digirolamo, Gregory James
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.