Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
NCT ID: NCT06102226
Last Updated: 2023-10-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
7000 participants
OBSERVATIONAL
2021-07-01
2024-12-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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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
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
Interventions
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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
Eligibility Criteria
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Inclusion Criteria
18 Years
80 Years
ALL
Yes
Sponsors
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Yong Zeng
OTHER
Responsible Party
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Yong Zeng
Beijing Anzhen Hospital
Principal Investigators
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Yong Zeng
Role: PRINCIPAL_INVESTIGATOR
Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen Hospital
Locations
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Yong Zeng
Beijing, Beijing Municipality, China
Countries
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Central Contacts
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Facility Contacts
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References
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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 May 16:heartjnl-2024-325486. doi: 10.1136/heartjnl-2024-325486. Online ahead of print.
Other Identifiers
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121100004006885458
Identifier Type: -
Identifier Source: org_study_id
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