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Identifying Vulnerable CoronAry PLaqUes With Artificial IntElligence-assisted CT Angiography
Sponsor: Jinling Hospital, China
Summary
The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are: 1. Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers; 2. Whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the reference standard. 3. Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD. 4. Whether the AI model enables to influnece downstream clincial decision-making.
Official title: Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
2000
Start Date
2023-07-01
Completion Date
2027-12-31
Last Updated
2026-05-11
Healthy Volunteers
No
Interventions
Intravascular imaging test
Coronary artery disease patients first underwent CCTA and then intravascular imaging test within 3 months.
Coronary plaque assessment
Plaques on coronary CT angiography (CCTA) were quantified and characterized using the developed AI model.
Locations (1)
Research Institute Of Medical Imaging Jinling Hospital
Nanjing, Jiangsu, China