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

Recruiting

• Conditions: Coronary Artery Disease; Artificial Heart Device User
• Interventions: Diagnostic Test: coronary artery imaging (coronary CTA or coronary angiography)

• Registration Number: NCT06102226
• Lead Sponsor: Yong Zeng

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-07-01
• Status Verified: 2023-10
• Study First Submitted: 2023-10-22
• Study First Submitted QC: 2023-10-22
• Last Update Submitted: 2023-10-22
• Study First Posted: 2023-10-26
• Last Update Posted: 2023-10-26
• Primary Completion: 2024-08-01
• Study Completion: 2024-12-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 7000

Inclusion Criteria

Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.

Exclusion Criteria

The exclusion criteria were as follows: (i) prior percutaneous coronary intervention (PCI); (ii) prior coronary artery bypass graft (CABG); (iii) other heart disease (e.g., congenital heart disease, valvular heart disease, or macrovascular disease); (iv) inability to have photographs taken; and (v) and a diagnosis of ST-segment elevation myocardial infarction (STEMI). Prior to the coronary angiography procedure, all eligible patients provided informed consent to participate in the study and to have their photographs used for research purposes.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
coronary artery disease group / non- coronary artery disease group	coronary artery imaging (coronary CTA or coronary angiography)	Recruited patients were categorized into a coronary artery disease group and a non-coronary artery disease group on the basis of coronary angiography findings, and the presence of CAD was defined as the presence of a coronary artery lesion with a stenosis

Primary Outcome Measures

Name	Time	Method
AUC	December 30, 2024	To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated

Secondary Outcome Measures

Name	Time	Method
specificity	December 30, 2024	To evaluate the algorithm performance, the specificity were calculated
sensitivity	December 30, 2024	To evaluate the algorithm performance, the sensitivity were calculated

Trial Locations

Locations (1)

Yong Zeng; 🇨🇳 Beijing, 北京, China