Improving cancer detection using advanced machine learning for medical images
Enabling Next Generation Machine Learning for Large Scale Image Analysis
This study is working on using smart computer technology to help doctors find cancer more accurately by analyzing medical images like CT scans, so patients can get better and faster diagnoses.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rnet Technologies, INC. NIH-funded |
| Lab location | 1 site (Dayton, UNITED STATES) |
| Project ID | NIH-10850949 on NIH RePORTER |
What this research studies
This research focuses on enhancing the accuracy of cancer detection through the use of advanced machine learning techniques applied to large-scale medical images, such as CT scans and digital pathology images. By utilizing patient-level labels, the project aims to create extensive training datasets that can improve the performance of AI models in identifying cancerous conditions. The approach leverages powerful GPU technology and machine learning frameworks to analyze vast amounts of imaging data efficiently, ultimately aiming to provide more reliable diagnostic tools for healthcare professionals.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals undergoing imaging procedures for cancer detection, particularly those with prostate or lung cancer.
Not a fit: Patients who do not require imaging for cancer detection or those with conditions not related to the focus of this research may not benefit.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely cancer diagnoses, improving patient outcomes and treatment options.
How similar studies have performed: Previous research has shown promising results in using machine learning for medical image analysis, indicating that this approach has the potential for significant advancements in cancer detection.
Where this research is happening
Dayton, UNITED STATES
- Rnet Technologies, INC. — Dayton, United States (Active)
Researchers
- Principal investigator: Sabin, Gerald — Rnet Technologies, INC.
- Study coordinator: Sabin, Gerald
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.