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 Details — Status: Recruiting

Administrative data

• NCT number: NCT06102226
• Other study ID #: 121100004006885458
• Status: Recruiting
• Start date: July 1, 2021
• Est. completion date: December 30, 2024

Study information

• Verified date: October 2023
• Source: Beijing Anzhen Hospital
• Contact: yong zeng, Dr
• Phone: +8613501373114
• Email: yzeng_anzhen[@]163.com
• Is FDA regulated: No
• Study type: Observational

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 7000
• Est. completion date: December 30, 2024
• Est. primary completion date: August 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Heart Device User
• Coronary Artery Disease
• Coronary Disease
• Myocardial Ischemia

Intervention

Diagnostic Test:
• coronary artery imaging (coronary CTA or coronary angiography)
In order to obtain the gold standard labeling for coronary heart disease, this topic will form a panel of experts on labeling, and the diagnosis will be based on coronary angiography, defined as a lesion with a stenosis of at least 50% in at least one coronary artery

Locations

Country	Name	City	State
China	Yong Zeng	Beijing	??

Sponsors (1)

Lead Sponsor	Collaborator
Yong Zeng

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC	To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated	December 30, 2024
Secondary	sensitivity	To evaluate the algorithm performance, the sensitivity were calculated	December 30, 2024
Secondary	specificity	To evaluate the algorithm performance, the specificity were calculated	December 30, 2024