Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.

ABSTRACT:

Objectives

This study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory.

Background

ML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories.

Methods

OCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80).

SUBMITTER: Cha JJ 

PROVIDER: S-EPMC9905417 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature