N
Nicolas Duchateau
Researcher at University of Lyon
Publications - 95
Citations - 1820
Nicolas Duchateau is an academic researcher from University of Lyon. The author has contributed to research in topics: Population & Cardiac resynchronization therapy. The author has an hindex of 21, co-authored 79 publications receiving 1363 citations. Previous affiliations of Nicolas Duchateau include Pompeu Fabra University & University of Barcelona.
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Journal ArticleDOI
Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography.
Mathieu De Craene,Gemma Piella,Oscar Camara,Nicolas Duchateau,Etelvino Silva,Adelina Doltra,Jan D'hooge,Josep Brugada,Marta Sitges,Alejandro F. Frangi,Alejandro F. Frangi +10 more
TL;DR: TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT), showing the potential of the proposed algorithm for the assessment of CRT.
Journal ArticleDOI
Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.
Maja Cikes,Sergio Sanchez-Martinez,Brian Claggett,Nicolas Duchateau,Gemma Piella,Constantine Butakoff,A.C. Pouleur,Dorit Knappe,Tor Biering-Sørensen,Tor Biering-Sørensen,Valentina Kutyifa,Arthur J. Moss,Kenneth M. Stein,Scott D. Solomon,Bart Bijnens +14 more
TL;DR: This work tested the hypothesis that a machine learning algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).
Journal ArticleDOI
3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation
TL;DR: A method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice to the bottom slice iteratively using a novel variant of the U-net.
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Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.
Partho P. Sengupta,Sirish Shrestha,B. Berthon,Emmanuel Messas,Erwan Donal,Geoffrey H. Tison,James K. Min,Jan D'hooge,Jens-Uwe Voigt,Joel T. Dudley,Johan W. Verjans,Khader Shameer,Kipp W. Johnson,Lasse Lovstakken,Mahdi Tabassian,Marco Piccirilli,Mathieu Pernot,Naveena Yanamala,Nicolas Duchateau,Nobuyuki Kagiyama,Olivier Bernard,Piotr J. Slomka,Rahul C. Deo,Rima Arnaout +23 more
TL;DR: An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases and summarizes a list of reporting items as an itemized checklist.
Journal ArticleDOI
Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction
Sergio Sanchez-Martinez,Nicolas Duchateau,Tamas Erdei,Gabor Kunszt,Svend Aakhus,Anna Degiovanni,Paolo Marino,Erberto Carluccio,Gemma Piella,Alan G. Fraser,Bart Bijnens +10 more
TL;DR: The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.