P
Pablo Lamata
Researcher at King's College London
Publications - 186
Citations - 4494
Pablo Lamata is an academic researcher from King's College London. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 33, co-authored 142 publications receiving 3286 citations. Previous affiliations of Pablo Lamata include University of Auckland & St Thomas' Hospital.
Papers
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Journal ArticleDOI
Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function.
Adam J. Lewandowski,Daniel Augustine,Pablo Lamata,Esther F. Davis,Merzaka Lazdam,Jane M Francis,Kenny McCormick,Andrew R. Wilkinson,Atul Singhal,Alan Lucas,Nic Smith,Stefan Neubauer,Paul Leeson +12 more
TL;DR: In this paper, the authors determined whether preterm birth is associated with a distinct left ventricular structure and function in humans, and then determined whether perinatal factors modify these left-varian parameters.
Journal ArticleDOI
The 'Digital Twin' to enable the vision of precision cardiology.
Jorge Corral-Acero,Francesca Margara,Maciej Marciniak,Cristobal Rodero,Filip Loncaric,Yingjing Feng,Andrew Gilbert,Joao Filipe Fernandes,Hassaan A. Bukhari,Ali Wajdan,Manuel Villegas Martinez,Mariana Sousa Santos,Mehrdad Shamohammdi,Hongxing Luo,Philip Westphal,Paul Leeson,Paolo DiAchille,Viatcheslav Gurev,Manuel Mayr,Liesbet Geris,Pras Pathmanathan,Tina M. Morrison,Richard Cornelussen,Frits W. Prinzen,Tammo Delhaas,Ada Doltra,Marta Sitges,Edward J. Vigmond,Ernesto Zacur,Vicente Grau,Blanca Rodriguez,Espen W. Remme,Steven A. Niederer,Peter Mortier,Kristin McLeod,Mark Potse,Esther Pueyo,Alfonso Bueno-Orovio,Pablo Lamata +38 more
TL;DR: It is argued that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient.
Book ChapterDOI
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
TL;DR: In this article, a recurrent fully convolutional network (RFCN) is proposed to learn image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units.
Journal ArticleDOI
Cardiovascular magnetic resonance feature-tracking assessment of myocardial mechanics: Intervendor agreement and considerations regarding reproducibility.
Andreas Schuster,Andreas Schuster,Vera-Christine Stahnke,Christina Unterberg-Buchwald,Johannes T. Kowallick,Pablo Lamata,Pablo Lamata,Michael Steinmetz,Shelby Kutty,Martin Fasshauer,Wieland Staab,Jan M Sohns,Boris Bigalke,Christian Ritter,Gerd Hasenfuß,Philipp Beerbaum,Jeffrey C. Lotz +16 more
TL;DR: CMR-FT strain and torsion measurements are subject to considerable intervendor variability, which can be reduced using three analysis repetitions, and warrants further investigation of incremental clinical merit.
Journal ArticleDOI
Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy.
Steven A. Niederer,Gernot Plank,Phani Chinchapatnam,Matthew Ginks,Matthew Ginks,Pablo Lamata,Kawal Rhode,Christopher A. Rinaldi,Christopher A. Rinaldi,Reza Razavi,Reza Razavi,Nicolas P. Smith,Nicolas P. Smith +12 more
TL;DR: In individuals with effective Frank-Starling mechanism, the length dependence of tension facilitates the homogenization of stress and strain, and in these individuals, synchronizing electrical activation through CRT may have minimal benefit.