Accuracy of automatic abnormal potential annotation for substrate identification in scar-related ventricular tachycardia.

Yosuke Nakatani Philippe MauryAnne RollinF Daniel RamirezCyril GoujeauTakashi NakashimaClémentine AndréAline CarapezziPhilipp KrisaiTakamitsu TakagiTsukasa KamakuraKonstantinos VlachosGhassen ChnitiRomain TixierQuentin Voglimacci-StefanopoliNicolas WelteRemi ChauvelJosselin DuchateauThomas PambrunNicolas DervalMélèze HociniMichel HaïssaguerrePierre JaïsFrédéric Sacher

Published in: Journal of cardiovascular electrophysiology (2021)

The Lumipoint algorithm was as/more accurate than manual annotation in 79% of patients. FP annotation detected LAVAs most accurately regardless of mapping rhythm and scar location. The accuracy of LP and DP annotations varied depending on mapping rhythm or scar location.

Keyphrases

high resolution
end stage renal disease
machine learning
rna seq
atrial fibrillation
newly diagnosed
chronic kidney disease
heart rate
wound healing
blood pressure
patient reported outcomes
climate change
drug induced
bioinformatics analysis
structural basis