A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

Marcelo Dantas Tavares de MeloJose de Arimatéia Batista Araujo-FilhoJosé Raimundo BarbosaCamila RoconCarlos Danilo Miranda RegisAlex Dos Santos FelixRoberto Kalil FilhoEdimar Alcides BocchiLudhmila Abrahão HajjarMahdi TabassianJan D'hoogeVera Maria Cury Salemi

Published in: PloS one (2021)

In this study, a random forest algorithm was capable of selecting the best echocardiographic predictors to RBR pattern in NCC patients, which was consistent with worse systolic, diastolic, and myocardium deformation indices. Prospective studies are warranted to evaluate the role of this tool for NCC risk stratification.

Keyphrases

left ventricular
machine learning
ejection fraction
heart failure
blood pressure
end stage renal disease
chronic kidney disease
newly diagnosed
climate change
deep learning
prognostic factors
mitral valve
left atrial
artificial intelligence
big data
patient reported