Clinical trial NCT06092801: Prediction of Coronary Artery Disease Based on Multimodal, Non-contact Information With Artificial Intelligence
Official title: Development and Validation of Artificial Intelligence Prediction Models Based on Multimodal, Non-contact Captured Information in Predicting Coronary Artery Disease
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Artery Disease
Lead sponsor: China National Center for Cardiovascular Diseases
Sponsor class: OTHER_GOV
First posted: 2023-10-23
Enrollment: 998
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals suspected of coronary artery disease and referred for confirmatory evaluation

Brief summary: The goal of this observational study are 1) to assess the effectiveness of modalities and/or their combination of multimodal non-contact information in predicting coronary artery disease; 2) to prospectively validate the performance of the developed artificial Intelligence models in predicting coronary artery disease.

Detailed description: This observational study aims to assess the effectiveness and potential mechanism of modalities of non-contact captured bio-physiological information, including facial RGB information, infrared thermography temperature information, gait information, and wearable device information, individually and/or in combination, in predicting coronary artery disease (CAD) with artificial intelligence technology.

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.

Eligibility criteria: Inclusion Criteria:

* Suspected individuals referred to for coronary angiography or coronary computer tomography angiography.

Exclusion Criteria:

* Prior percutaneous coronary intervention (PCI)
* 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

Primary outcomes:
measure: Sensitivity of algorithm
timeFrame: At the end of enrollment (1 mouth)
measure: Specificity of algorithm
timeFrame: At the end of enrollment (1 mouth)
Secondary outcomes:
measure: Area under receiver operating curve (AUC)
timeFrame: At the end of enrollment (1 mouth)
Other outcomes:
measure: Positive predictive value (PPV) of algorithm
timeFrame: At the end of enrollment (1 mouth)
measure: Negative predictive value (NPV)
timeFrame: At the end of enrollment (1 mouth)
measure: Diagnostic accuracy rate
timeFrame: At the end of enrollment (1 mouth)
Locations:
city: Beijing
country: China
facility: Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
state: Beijing
Study officials:
affiliation: Chinese Academy of Medical Sciences, Fuwai Hospital
name: Shen Lin, M.D., Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.