Relation Between AI-QCA and Cardiac PET (AI-CARPET)

Chonnam National University

Status: Completed

Conditions: Coronary Artery Disease; Coronary Artery Stenosis

Treatments: Device: Percutaneous coronary intervention (PCI)

Study type: Observational

Funder types: Other

Identifiers

NCT06397820

CNUH-AI-CARPET

Details and patient eligibility

About

The aim of the study is to evaluate the clinical implications of artificial Intelligence (AI)-assisted quantitative coronary angiography (QCA) and positron emission tomography (PET)-derived myocardial blood flow in clinically indicated patients.

Full description

Percutaneous coronary angiography (CAG) is a standard method for evaluating coronary artery disease. Traditionally, a reduction in the luminal diameter of the coronary arteries by 50% or more during angiography has been considered a significant stenotic lesion. However, the assessment of coronary artery stenosis is usually based on visual estimation by the operator in daily routine clinical practice, which interferes with the objective evaluation.

Quantitative coronary angiography (QCA) has been developed to overcome this limitation. This technique involves the software-based analysis of coronary images obtained through CAG. The previous study showed that there was low concordance between the QCA and visual estimation of coronary artery stenosis (Kappa=0.63) and a reclassification rate of approximately 20%. Furthermore, visual assessments tended to overestimate the degree of coronary artery stenosis, particularly in complex lesions such as bifurcation lesions.

However, there are some limitations to adopting QCA in our daily routine practice. The QCA cannot analyze coronary images on-site and is not fully automated, requiring manual adjustments by humans. Recent advancements have led to the development of artificial intelligence (AI)-based QCA software, which achieves complete automation in the analysis process and provides real-time objective evaluations of coronary artery stenosis.

This study aims to examine the clinical significance of AI-QCA by assessing the correlation between the degree of coronary stenosis detected by AI-QCA and myocardial blood flow abnormalities observed in 13NH3-Ammonia PET scans in patients with coronary artery disease.

Enrollment: 168 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

Inclusion criteria

1. Subject must be ≥18 years
2. Patients suspected with CAD or ischemic heart disease
3. Patients undergoing CAG and cardiac PET for evaluation of severity of coronary artery disease

Exclusion criteria

1. Poor imaging quality of CAG and PET which were not available for core-lab analysis
2. Chronic total occlusion
3. Time interval was more than >3 months between CAG and PET
4. History of coronary artery bypass grafting
5. History of acute myocardial infarction or recent myocardial infarction
6. Heart failure (left ventricular ejection fraction <40%)

Trial design

168 participants in 2 patient groups

Positive for PET-derived indexes

Description:

Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET

Treatment:

Device: Percutaneous coronary intervention (PCI)

Negative for PET-derived indexes

Description:

Patients who had preserved stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET