Validation of artificial intelligence-based quantitative coronary angiography.

ABSTRACT:

Background

Coronary angiography is fundamental for the diagnosis and treatment of coronary artery disease. Manual quantitative coronary angiography (QCA) is accurate and reproducible; however, it is time-consuming and labor-intensive. However, recent advancements in artificial intelligence (AI) have enabled automated and rapid analysis of medical images, addressing the need for real-time quantitative coronary analysis.

Aims

This study aimed to evaluate the accuracy of AI-based QCA (AI-QCA) compared with that via manual QCA and clinician acceptance.

Methods

This retrospective, single-center study was conducted in two phases. Phase 1 was a pilot study comparing AI-QCA with manual QCA and visual estimation. It involved 15 patients who underwent coronary angiography at Se

SUBMITTER: Kim DH 

PROVIDER: S-EPMC11653446 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature