Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 3:27 AM
Ignite Modification Date:
2025-12-25 @ 3:27 AM
NCT ID:
NCT06025305
Brief 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.
Detailed Description:
Coronary artery disease (CAD) remains the leading cause of death worldwide. Atherosclerotic plaques play a pivotal role in CAD-related patient mortality. Thus, the detection, quantification, and characterization of coronary plaques are clinically significant for early prevention and interventions for CAD.
Coronary CT angiography (CCTA) has emerged as a robust noninvasive tool for the evaluation of CAD. In clinical practice, the coronary plaque assessment is performed by a time-consuming manual process dependent on the clinician's experience and subjective visual interpretation. With the development of artificial intelligence, many automatic computer-aided methods have been proposed to post-process the CCTA images. However, previously proposed algorithms of plaque evaluation were not developed based on intravascular ultrasound (IVUS) or optical coherence tomography (OCT), which were regarded as the gold reference for plaque evaluation. Thus, we aimed to develop a deep learning model in a whole-process automatic and intelligent system on CCTA to detect, quantify, and characterize plaques using IVUS or OCT as reference standard. Then we will work on the validation in different clinical scenarios: (1) Validation of the accuracy of the new deep learning model; (2) Prognosis of the model in different populations with CAD.
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 IVUS or OCT 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 coronary artery disease (China CT-FFR study 2).
Study:
NCT06025305
Study Brief:
Protocol Section:
NCT06025305