Titre

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance.

Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Bonnemain, J.
Auteure/Auteur
Zeller, M.
Auteure/Auteur
Pegolotti, L.
Auteure/Auteur
Deparis, S.
Auteure/Auteur
Liaudet, L.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
2297-055X
Statut éditorial
Publié
Date de publication
2021
Volume
8
Première page
752088
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters of LV systolic function. DNN input data were systemic and pulmonary arterial pressure signals, and rotation speeds of the device. Output data were parameters of LV systolic function, including end-systolic maximal elastance (E <sub>max,lv</sub> ), a variable essential for adequate hemodynamic assessment of the LV. A 0D model of the cardiovascular system, including a wide range of LVAD settings and incorporating the whole spectrum of heart failure, was used to generate data for the training procedure of the DNN. The DNN predicted E <sub>max,lv</sub> with a mean relative error of 10.1%, and all other parameters of LV function with a mean relative error of <13%. The framework was then able to retrieve a number of LV physiological variables (i.e., pressures, volumes, and ejection fraction) with a mean relative error of <5%. Our method provides an innovative tool to assess LV hemodynamics under device assistance, which could be helpful for a better understanding of LV-LVAD interactions, and for therapeutic optimization.
Sujets

cardiovascular modeli...

deep neural network

heart failure

left ventricular assi...

machine learning

PID Serval
serval:BIB_DA745B7EE75C
DOI
PMID
WOS
Permalien
Open Access
Oui
Date de création
2021-12-03T12:10:50.598Z
Date de création dans IRIS
2025-05-20T21:27:09Z
Fichier(s)
En cours de chargement...
Vignette d'image
Nom

34765658_BIB_DA745B7EE75C.pdf

Version du manuscrit

published

Licence

https://creativecommons.org/licenses/by/4.0

Taille

1.29 MB

Format

Adobe PDF

PID Serval

serval:BIB_DA745B7EE75C.P001

URN

urn:nbn:ch:serval-BIB_DA745B7EE75C2

Somme de contrôle

(MD5):b48f9e36c9fef3602a7a8e2d0ec3b11a