Using machine learning to improve noninvasive liver disease diagnosis
Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD
This study is working on a new way to help doctors spot non-alcoholic fatty liver disease more easily and safely, using a mix of different health information and images, so that patients at risk of serious liver problems can get the care they need without needing painful tests.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-10542745 on NIH RePORTER |
What this research studies
This research focuses on developing a machine learning model that integrates various types of data, including clinical, laboratory, sonographic, and elastographic information, to enhance the diagnosis of non-alcoholic fatty liver disease (NAFLD). The goal is to create a non-invasive method for identifying patients at high risk of liver damage, particularly those with high-risk non-alcoholic steatohepatitis (hrNASH). By expanding a patient database and analyzing thousands of images and clinical data points, the research aims to provide a more accurate and accessible diagnostic tool for liver disease. This could help in early detection and management of liver conditions without the need for invasive procedures like biopsies.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals diagnosed with non-alcoholic fatty liver disease, particularly those at high risk for liver damage.
Not a fit: Patients with liver diseases that are not related to non-alcoholic fatty liver disease may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to safer and more accurate diagnosis of liver diseases, allowing for timely treatment and better patient outcomes.
How similar studies have performed: Other research has shown promise in using machine learning for medical diagnostics, indicating that this approach could be effective in improving liver disease diagnosis as well.
Where this research is happening
Boston, United States
- Massachusetts General Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Samir, Anthony Edward — Massachusetts General Hospital
- Study coordinator: Samir, Anthony Edward
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.