Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD
Description: Project Narrative In this project, we aim to create low-cost non-invasive technology to diagnose people with high risk non-alcoholic steatohepatitis (hrNASH), a common liver disease that has a high risk of cirrhosis. We will accomplish this goal by improving liver ultrasound imaging and by creating a prediction tool that integrates liver ultrasound, laboratory testing and clinical information. This prediction tool has the potential to reduce the need for liver biopsies.
Funding type: research
Funder scheme: R01
Start date: 2019-01-20
End date: 2025-12-31
Investigator: ANTHONY EDWARD SAMIR
Institution: MASSACHUSETTS GENERAL HOSPITAL
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Funded outputs count: 19
Funder award ID: R01DK119860
OpenAlex ID: G1891753662