Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review.

ABSTRACT:

Background

The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFR_CT), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with invasive FFR (iFFR). This systematic review aims to summarize the current evidence on the diagnostic accuracy of (ML-FFR_CT) compared with iFFR for diagnosis of patient- and vessel-level coronary ischemia.

Methods

To identify suitable studies, comprehensive literature search was performed in PubMed, the Cochrane Library, Embase, up to August 2023. The index test was ML derived FFR and studies with diagnostic test accuracy data of ML-FFR_CT at a thresho

SUBMITTER: van Noort D 

PROVIDER: S-EPMC11795686 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature