Using foundation model for cardiovascular disease detection.

Principal Investigator

Name
Xiaofeng Yang

Degrees
Ph.D

Institution
Emory University

Position Title
Associate Professor

Email
xyang43@emory.edu

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-1249

Initial CDAS Request Approval
May 16, 2024

Title
Using foundation model for cardiovascular disease detection.

Summary
1. Introduction
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Accurate and early diagnosis is crucial for reducing the burden of CVDs and improving patient outcomes. However, standard computed tomography (CT) scans expose patients to high radiation levels. Low-dose CT (LDCT) imaging is increasingly used to minimize radiation exposure, yet it often leads to reduced image quality, affecting diagnostic accuracy. Recent advancements in foundation models for medical image analysis offer a promising approach to overcome these challenges.

2. Objective
The primary objective of this proposal is to develop a foundation model, (e.g., Chat-GPT) for accurately classifying cardiovascular diseases on low-dose CT images. This approach will leverage the transfer learning and representation learning capabilities of foundation models, resulting in accurate and robust classification despite the limitations posed by low-dose CT.

Aims

1. Investigate foundation model in cardiovascular disease classification
2. Find a reliable method to have great performance cardiovascular disease classification -far better than current methods.

Collaborators

Xu Xin, Xiaofeng Yang,