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The 'Digital Twin' to enable the vision of precision cardiology.
Corral-Acero, Jorge; Margara, Francesca; Marciniak, Maciej; Rodero, Cristobal; Loncaric, Filip; Feng, Yingjing; Gilbert, Andrew; Fernandes, Joao F; Bukhari, Hassaan A; Wajdan, Ali; Martinez, Manuel Villegas; Santos, Mariana Sousa; Shamohammdi, Mehrdad; Luo, Hongxing; Westphal, Philip; Leeson, Paul; DiAchille, Paolo; Gurev, Viatcheslav; Mayr, Manuel; Geris, Liesbet; Pathmanathan, Pras; Morrison, Tina; Cornelussen, Richard; Prinzen, Frits; Delhaas, Tammo; Doltra, Ada; Sitges, Marta; Vigmond, Edward J; Zacur, Ernesto; Grau, Vicente; Rodriguez, Blanca; Remme, Espen W; Niederer, Steven; Mortier, Peter; McLeod, Kristin; Potse, Mark; Pueyo, Esther; Bueno-Orovio, Alfonso; Lamata, Pablo.
Afiliación
  • Corral-Acero J; Department of Engineering Science, University of Oxford, Oxford, UK.
  • Margara F; Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK.
  • Marciniak M; Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Rodero C; Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Loncaric F; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
  • Feng Y; IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France.
  • Gilbert A; IMB, UMR 5251, University of Bordeaux, Talence F-33400, France.
  • Fernandes JF; GE Vingmed Ultrasound AS, Horton, Norway.
  • Bukhari HA; Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Wajdan A; IMB, UMR 5251, University of Bordeaux, Talence F-33400, France.
  • Martinez MV; Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain.
  • Santos MS; The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
  • Shamohammdi M; The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
  • Luo H; FEops NV, Ghent, Belgium.
  • Westphal P; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Leeson P; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • DiAchille P; Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands.
  • Gurev V; Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK.
  • Mayr M; Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
  • Geris L; Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
  • Pathmanathan P; King's British Heart Foundation Centre, King's College London, London, UK.
  • Morrison T; Virtual Physiological Human Institute, Leuven, Belgium.
  • Cornelussen R; Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
  • Prinzen F; Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
  • Delhaas T; Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands.
  • Doltra A; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Sitges M; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Vigmond EJ; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
  • Zacur E; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
  • Grau V; CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain.
  • Rodriguez B; IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France.
  • Remme EW; IMB, UMR 5251, University of Bordeaux, Talence F-33400, France.
  • Niederer S; Department of Engineering Science, University of Oxford, Oxford, UK.
  • Mortier P; Department of Engineering Science, University of Oxford, Oxford, UK.
  • McLeod K; Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK.
  • Potse M; The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
  • Pueyo E; Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
  • Bueno-Orovio A; FEops NV, Ghent, Belgium.
  • Lamata P; GE Vingmed Ultrasound AS, Horton, Norway.
Eur Heart J ; 41(48): 4556-4564, 2020 12 21.
Article en En | MEDLINE | ID: mdl-32128588
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Cardiología Tipo de estudio: Prognostic_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Eur Heart J Año: 2020 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Cardiología Tipo de estudio: Prognostic_studies Aspecto: Patient_preference Límite: Humans Idioma: En Revista: Eur Heart J Año: 2020 Tipo del documento: Article Pais de publicación: Reino Unido