Risk Evaluation by Coronary Imaging and Artificial Intelligence-Based Functional Analysis Integrated Plaque Analysis Using Coronary CT Angiography and Intracoronary Imaging

What this Trial Is About

Researchers are conducting a single-center, prospective study to analyze coronary artery plaque characteristics and local blood flow in patients diagnosed with chronic coronary syndrome (CCS) or non-ST-segment elevation acute coronary syndrome (NSTE-ACS). The study focuses on patients with marginal or obstructive lesions in major coronary arteries, aiming to improve the accuracy of plaque assessment using 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 develop a new CCTA-based model to better identify high-risk plaques. Participants will undergo noninvasive CCTA scans followed by invasive coronary angiography and intracoronary imaging within one month. The study will use advanced methods such as co-registration, feature extraction, radiomic analysis, and machine learning to enhance plaque analysis. The new model will be compared with traditional CTA models to assess its ability to detect high-risk plaques and predict major adverse cardiovascular events. Patients will be followed every three months for two years to monitor outcomes. Researchers will evaluate the agreement between different imaging techniques and track the prediction performance of the new high-risk plaque model over this period. The study includes regular assessments to ensure participant safety and adherence, with the total participation lasting two years.

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