a Foundational Model for Cardiovascular Disease Diagnosis and Prediction

Overview

The goal of this observational study is to develop and evaluate the efficacy of a foundational model that integrates multimodal medical data to improve the diagnosis and prediction of cardiovascular diseases in patients aged 18 and older, including those with various heart conditions such as coronary artery disease, heart failure, and arrhythmias. The main questions it aims to answer are: Can a multimodal data-based diagnostic model match or exceed the accuracy of traditional gold-standard methods like coronary angiography, MRI, and echocardiography? Does integrating different types of data (ECG, imaging, biochemical tests) improve diagnostic accuracy and prediction of cardiovascular disease outcomes? Researchers will compare the foundational model with traditional diagnostic methods to see if the model offers better sensitivity, specificity, and prediction accuracy across different heart disease types. Participants will: Provide data from past medical records, including ECG, echocardiography, cardiac MRI, and biochemical tests. Undergo further data collection if necessary, in line with standard clinical procedures for cardiovascular disease management.

This study aims to develop and validate a foundational model that uses multimodal medical data for the diagnosis and prediction of cardiovascular diseases. By integrating data from ECG, echocardiography, cardiac MRI, CTA, nuclear imaging (SPECT/PET), and biochemical tests, the model seeks to improve diagnostic accuracy and predict disease outcomes. Study Design Study Type: Retrospective, multicenter, observational study Study Population: Adults aged 18 and older, including patients with coronary artery disease (CAD), heart failure, arrhythmias, and valvular heart disease (VHD). Objectives Primary Objective: To create a model that improves the diagnosis and prediction of cardiovascular diseases using multimodal data. Secondary Objective: To compare the performance of the model against traditional diagnostic methods like coronary angiography, echocardiography, and MRI. Methodology Data from 2009 to 2023 will be collected from multiple hospitals. The model will use deep learning techniques to integrate the data for more accurate diagnosis and prediction. The performance of the model will be compared with current gold-standard methods. Expected Outcomes Improved diagnostic accuracy and early disease detection. Enhanced prediction of long-term outcomes, allowing for better treatment planning.