Risk Evaluation by Coronary Imaging and Artificial Intelligence-Based Functional Analyzing Techniques: Integrated Plaque Analysis by Coronary CT Angiography and Intracoronary Imaging.

What this Trial Is About

Researchers are studying patients with chronic coronary syndrome (CCS) or non-ST-segment elevation acute coronary syndrome (NSTE-ACS) to better understand coronary artery plaque characteristics and local blood flow. The study focuses on those with marginal or obstructive lesions in major coronary arteries. By combining multiple imaging methods, including coronary CT angiography (CCTA) and intracoronary imaging techniques like intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near infrared spectroscopy (NIRS), the goal is to improve plaque analysis accuracy and develop a new model to identify high-risk plaques using artificial intelligence and imaging data. Participants undergo a CCTA scan to detect marginal or obstructive coronary lesions, followed within one month by invasive coronary angiography and intracoronary imaging examinations such as IVUS, OCT, and NIRS. The study uses advanced techniques like co-registration, feature extraction, radiomic analysis, and machine learning to compare imaging results and train a new CCTA-based high-risk plaque model. This prospective cohort study is conducted at a single center and includes ongoing monitoring and follow-up. During the study, patients are followed every three months for two years to track cardiovascular events and outcomes. Researchers will evaluate how well the new model predicts high-risk plaques compared to traditional CCTA models, focusing on major adverse cardiovascular events (MACEs) such as cardiovascular death, myocardial infarction, and unplanned revascularization. The study involves detailed imaging assessments and long-term follow-up to improve understanding of coronary artery disease and its risks.

Risk Evaluation by COronary Imaging and Artificial intelliGence Based fuNctIonal analyZing tEchniques - III