Deep learning for prediction of fractional flow reserve from resting coronary pressure curves.

ABSTRACT:

Background

It would be ideal for a non-hyperaemic index to predict fractional flow reserve (FFR) more accurately, given FFR's extensive validation in a multitude of clinical settings.

Aims

The aim of this study was to derive a novel non-hyperaemic algorithm based on deep learning and to validate it in an internal validation cohort against FFR.

Methods

The ARTIST study is a post hoc analysis of three previously published studies. In a derivation cohort (random 80% sample of the total cohort) a deep neural network was trained (deep learning) with paired examples of resting coronary pressure curves and their FFR values. The resulting algorithm was validated against unseen resting pressure curves from a random 20% sample of the total cohort. The primary endpoint was diagn

SUBMITTER: Zimmermann FM 

PROVIDER: S-EPMC9890578 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature