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Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs

Coronary Artery Disease Clinical Trial

Official title:
Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs

Clinical Trial Summary

Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases. At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06102226
Study type Observational
Source Beijing Anzhen Hospital
Contact yong zeng, Dr
Phone +8613501373114
Email yzeng_anzhen@163.com
Status Recruiting
Phase
Start date July 1, 2021
Completion date December 30, 2024
View Details
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