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

Journal: EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology

PMID: 32863244

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.

Authors

Frederik M Zimmermann

Department of Cardiology, Catharina Hospital, Eindhoven, the Netherlands.
Thomas P Mast
Nils P Johnson
Ivo Everts
Barry Hennigan
Colin Berry

School of Cardiovascular and Metabolic Health, University of Glasgow, G12 8TA, UK.
Daniel T Johnson
Bernard De Bruyne

Cardiovascular Center OLV Aalst, Belgium.
William F Fearon
Keith Oldroyd
Nico H J Pijls
Pim A L Tonino
Marcel van 't Veer

Keywords

Artificial Intelligence Cardiac Catheterization Coronary Angiography Coronary Stenosis Coronary Vessels Deep Learning Fractional Flow Reserve, Myocardial Humans Predictive Value of Tests Reproducibility of Results Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (32863244)

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

Abstract

Authors

Keywords

External Resources

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