Clinical trial NCT06102226: Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
Official title: Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Artery Disease
Artificial Heart Device User
Lead sponsor: Beijing Anzhen Hospital
Sponsor class: OTHER
First posted: 2023-10-26
Enrollment: 7000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography

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.


Eligibility criteria: 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.

Primary outcomes:
measure: AUC
timeFrame: December 30, 2024
Secondary outcomes:
measure: sensitivity
timeFrame: December 30, 2024
measure: specificity
timeFrame: December 30, 2024
Other outcomes:
Locations:
city: Beijing
country: China
facility: Yong Zeng
state: 北京
Study officials:
affiliation: Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen Hospital
name: Yong Zeng
role: PRINCIPAL_INVESTIGATOR
References: