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.
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.
Study Type
OBSERVATIONAL
Enrollment
2,978
No intervention
Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, China
Sensitivity of algorithm
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
Time frame: At the end of enrollment (1 mouth)
Specificity of algorithm
Sensitivity of algorithm in predicting coronary artery disease assessed in test group
Time frame: At the end of enrollment (1 mouth)
Area under receiver operating curve (AUC)
Area under receiver operating curve of algorithm in predicting coronary artery disease assessed in test group
Time frame: At the end of enrollment (1 mouth)
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