Prediction of Coronary Artery Disease Based on Multimodal, Non-contact Information With Artificial Intelligence
NCT ID: NCT06092801
Last Updated: 2025-11-28
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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COMPLETED
2978 participants
OBSERVATIONAL
2023-11-20
2025-04-09
Brief Summary
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Detailed Description
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Individuals suspected of CAD and referred for evaluation will be invited to participate in the current study for analyzing the non-contact information and association with underlying CAD status, in order to establish the most efficient artificial Intelligence modeling strategy, and prospectively validate the predictive performance of the developed artificial Intelligence models for CAD prediction.
Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Study Groups
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Individuals suspected of coronary artery disease
Individuals suspected of coronary artery disease and referred for evaluation
No intervention
No intervention
Interventions
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No intervention
No intervention
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* Prior coronary artery bypass graft (CABG)
* Undergoing confirmatory coronary evaluation as pre-operation routines for other cardiac diseases
* With artificial body alteration (e.g. cosmetic surgery, facial trauma, or make-up) that may affect the non-contact information of study interest
* Age less than 18 years old
* Other circumstances that prevent participants from cooperating with the study process
* Decline to consent for study participation
18 Years
ALL
Yes
Sponsors
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China National Center for Cardiovascular Diseases
OTHER_GOV
Responsible Party
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Principal Investigators
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Shen Lin, M.D., Ph.D.
Role: PRINCIPAL_INVESTIGATOR
Chinese Academy of Medical Sciences, Fuwai Hospital
Locations
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Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, China
Countries
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References
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Lin S, Li Z, Fu B, Chen S, Li X, Wang Y, Wang X, Lv B, Xu B, Song X, Zhang YJ, Cheng X, Huang W, Pu J, Zhang Q, Xia Y, Du B, Ji X, Zheng Z. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. 2020 Dec 7;41(46):4400-4411. doi: 10.1093/eurheartj/ehaa640.
Other Identifiers
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2022-GSP-QN-10
Identifier Type: -
Identifier Source: org_study_id
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