Literature DB >> 32128588

The 'Digital Twin' to enable the vision of precision cardiology.

Jorge Corral-Acero1, Francesca Margara2, Maciej Marciniak3, Cristobal Rodero3, Filip Loncaric4, Yingjing Feng5,6, Andrew Gilbert7, Joao F Fernandes3, Hassaan A Bukhari6,8, Ali Wajdan9, Manuel Villegas Martinez9, Mariana Sousa Santos10, Mehrdad Shamohammdi11, Hongxing Luo11, Philip Westphal12, Paul Leeson13, Paolo DiAchille14, Viatcheslav Gurev14, Manuel Mayr15, Liesbet Geris16, Pras Pathmanathan17, Tina Morrison17, Richard Cornelussen12, Frits Prinzen11, Tammo Delhaas11, Ada Doltra4, Marta Sitges4,18, Edward J Vigmond5,6, Ernesto Zacur1, Vicente Grau1, Blanca Rodriguez2, Espen W Remme9, Steven Niederer3, Peter Mortier10, Kristin McLeod7, Mark Potse5,6,19, Esther Pueyo8,20, Alfonso Bueno-Orovio2, Pablo Lamata3.   

Abstract

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue 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. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
© The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.

Entities:  

Keywords:  Artificial intelligence; Computational modelling; Digital twin; Precision medicine

Mesh:

Year:  2020        PMID: 32128588      PMCID: PMC7774470          DOI: 10.1093/eurheartj/ehaa159

Source DB:  PubMed          Journal:  Eur Heart J        ISSN: 0195-668X            Impact factor:   29.983


  80 in total

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Review 2.  Promises, promises, and precision medicine.

Authors:  Michael J Joyner; Nigel Paneth
Journal:  J Clin Invest       Date:  2019-01-28       Impact factor: 14.808

Review 3.  Big data analytics to improve cardiovascular care: promise and challenges.

Authors:  John S Rumsfeld; Karen E Joynt; Thomas M Maddox
Journal:  Nat Rev Cardiol       Date:  2016-03-24       Impact factor: 32.419

4.  Incorporation of stochastic engineering models as prior information in Bayesian medical device trials.

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5.  Validating the Physiologic Model HumMod as a Substitute for Clinical Trials Involving Acute Normovolemic Hemodilution.

Authors:  Charles R Sims; Luis R Delima; Arthur Calimaran; Robert Hester; W Andrew Pruett
Journal:  Anesth Analg       Date:  2018-01       Impact factor: 5.108

6.  Differentiating Electromechanical From Non-Electrical Substrates of Mechanical Discoordination to Identify Responders to Cardiac Resynchronization Therapy.

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Journal:  Circ Cardiovasc Imaging       Date:  2015-09       Impact factor: 7.792

7.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.

Authors:  Riccardo Miotto; Li Li; Brian A Kidd; Joel T Dudley
Journal:  Sci Rep       Date:  2016-05-17       Impact factor: 4.379

Review 8.  Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine.

Authors:  Kipp W Johnson; Khader Shameer; Benjamin S Glicksberg; Ben Readhead; Partho P Sengupta; Johan L M Björkegren; Jason C Kovacic; Joel T Dudley
Journal:  JACC Basic Transl Sci       Date:  2017-06-26

9.  Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling.

Authors:  Yolanda R Hill; Nick Child; Ben Hanson; Mikael Wallman; Ruben Coronel; Gernot Plank; Christopher A Rinaldi; Jaswinder Gill; Nicolas P Smith; Peter Taggart; Martin J Bishop
Journal:  PLoS One       Date:  2016-03-02       Impact factor: 3.240

10.  The estimation of patient-specific cardiac diastolic functions from clinical measurements.

Authors:  Jiahe Xi; Pablo Lamata; Steven Niederer; Sander Land; Wenzhe Shi; Xiahai Zhuang; Sebastien Ourselin; Simon G Duckett; Anoop K Shetty; C Aldo Rinaldi; Daniel Rueckert; Reza Razavi; Nic P Smith
Journal:  Med Image Anal       Date:  2012-10-16       Impact factor: 8.545

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  55 in total

1.  Phenotyping heart failure using model-based analysis and physiology-informed machine learning.

Authors:  Edith Jones; E Benjamin Randall; Scott L Hummel; David M Cameron; Daniel A Beard; Brian E Carlson
Journal:  J Physiol       Date:  2021-10-18       Impact factor: 5.182

2.  Simulation of the 3D Hyperelastic Behavior of Ventricular Myocardium using a Finite-Element Based Neural-Network Approach.

Authors:  Wenbo Zhang; David S Li; Tan Bui-Thanh; Michael S Sacks
Journal:  Comput Methods Appl Mech Eng       Date:  2022-04-01       Impact factor: 6.756

3.  Computational analysis of cardiac structure and function in congenital heart disease: Translating discoveries to clinical strategies.

Authors:  Nickolas Forsch; Sachin Govil; James C Perry; Sanjeet Hegde; Alistair A Young; Jeffrey H Omens; Andrew D McCulloch
Journal:  J Comput Sci       Date:  2020-09-19

4.  A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions.

Authors:  Karim Kadry; Max L Olender; David Marlevi; Elazer R Edelman; Farhad R Nezami
Journal:  J R Soc Interface       Date:  2021-09-29       Impact factor: 4.293

Review 5.  Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Authors:  Chengyue Wu; Guillermo Lorenzo; David A Hormuth; Ernesto A B F Lima; Kalina P Slavkova; Julie C DiCarlo; John Virostko; Caleb M Phillips; Debra Patt; Caroline Chung; Thomas E Yankeelov
Journal:  Biophys Rev (Melville)       Date:  2022-05-17

Review 6.  Applications of artificial intelligence in cardiovascular imaging.

Authors:  Maxime Sermesant; Hervé Delingette; Hubert Cochet; Pierre Jaïs; Nicholas Ayache
Journal:  Nat Rev Cardiol       Date:  2021-03-12       Impact factor: 32.419

Review 7.  3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments.

Authors:  Ramtin Gharleghi; Claire A Dessalles; Ronil Lal; Sinead McCraith; Kiran Sarathy; Nigel Jepson; James Otton; Abdul I Barakat; Susann Beier
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Review 8.  Data integration for the numerical simulation of cardiac electrophysiology.

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9.  The impact of myocardial compressibility on organ-level simulations of the normal and infarcted heart.

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10.  Causes of altered ventricular mechanics in hypertrophic cardiomyopathy: an in-silico study.

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Journal:  Biomed Eng Online       Date:  2021-07-22       Impact factor: 2.819

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