Ignite Creation Date:
2024-05-06 @ 8:28 PM
Last Modification Date:
2024-10-26 @ 3:28 PM
Study NCT ID:
NCT06397820
Status:
RECRUITING
Last Update Posted:
2024-05-06
First Post:
2024-04-30
Brief Title:
Relation Between AI-QCA and Cardiac PET
Sponsor:
Chonnam National University Hospital
Organization:
Chonnam National University Hospital
Study Overview
Official Title:
Relation Between Artificial Intelligence AI-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI-CARPET
Brief Summary:
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
Detailed 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 Kappa063 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
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 Kappa063 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None