Using AI to predict cardiovascular disease
Principal Investigator
Name
yuankai wang
Degrees
Ph.D
Institution
University of Liverpool
Position Title
Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1110
Initial CDAS Request Approval
Aug 8, 2023
Title
Using AI to predict cardiovascular disease
Summary
The project aims to develop a predictive model for estimating the Coronary Artery Calcium (CAC) score using Computed Tomography (CT) images.
The CAC score is a well-established indicator of coronary artery disease and is widely used in cardiovascular risk assessment. The ability to accurately predict the CAC score from CT images can significantly aid in early detection and prevention of heart diseases, leading to improved patient outcomes and reduced healthcare costs.
The CAC score is a well-established indicator of coronary artery disease and is widely used in cardiovascular risk assessment. The ability to accurately predict the CAC score from CT images can significantly aid in early detection and prevention of heart diseases, leading to improved patient outcomes and reduced healthcare costs.
Aims
The primary objective of this project is to leverage state-of-the-art machine learning techniques and image analysis algorithms to create a robust and accurate model for predicting the CAC score from CT images.
Collaborators
University of Liverpool. Liverpool Heart and Chest Hospital