Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

ABSTRACT: Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard. A lesion was classified as 'detected' when the minimum lumen diameter (MLD) identified by manual QCA fell within the boundaries of the lesion delineated by AI-QCA or visual estimation. The detected lesions

SUBMITTER: Chae J 

PROVIDER: S-EPMC11880186 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature