Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
1 other identifier
observational
7,000
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2021
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 1, 2021
CompletedFirst Submitted
Initial submission to the registry
October 22, 2023
CompletedFirst Posted
Study publicly available on registry
October 26, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 1, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
December 30, 2024
CompletedOctober 26, 2023
October 1, 2023
3.1 years
October 22, 2023
October 22, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
AUC
To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated
December 30, 2024
Secondary Outcomes (2)
sensitivity
December 30, 2024
specificity
December 30, 2024
Study Arms (1)
coronary artery disease group / non- coronary artery disease group
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
Interventions
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
Eligibility Criteria
Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography
You may qualify if:
- Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Yong Zenglead
Study Sites (1)
Yong Zeng
Beijing, Beijing Municipality, 100029, China
Related Publications (1)
Ye Y, Feng W, Ding Y, Chen Q, Zhang Y, Lin L, Xia P, Ma T, Ju L, Wang B, Chang X, Wang X, Cai L, Ge Z, Zeng Y. Retinal image-based deep learning for mild cognitive impairment detection in coronary artery disease population. Heart. 2025 Oct 14;111(21):1013-1019. doi: 10.1136/heartjnl-2024-325486.
PMID: 40379470DERIVED
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Yong Zeng
Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Beijing Anzhen Hospital
Study Record Dates
First Submitted
October 22, 2023
First Posted
October 26, 2023
Study Start
July 1, 2021
Primary Completion
August 1, 2024
Study Completion
December 30, 2024
Last Updated
October 26, 2023
Record last verified: 2023-10
Data Sharing
- IPD Sharing
- Will not share