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Digital Twins for Multiple Sclerosis
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128142/ https://www.ncbi.nlm.nih.gov/pubmed/34012452 http://dx.doi.org/10.3389/fimmu.2021.669811 |
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author | Voigt, Isabel Inojosa, Hernan Dillenseger, Anja Haase, Rocco Akgün, Katja Ziemssen, Tjalf |
author_facet | Voigt, Isabel Inojosa, Hernan Dillenseger, Anja Haase, Rocco Akgün, Katja Ziemssen, Tjalf |
author_sort | Voigt, Isabel |
collection | PubMed |
description | An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life. |
format | Online Article Text |
id | pubmed-8128142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81281422021-05-18 Digital Twins for Multiple Sclerosis Voigt, Isabel Inojosa, Hernan Dillenseger, Anja Haase, Rocco Akgün, Katja Ziemssen, Tjalf Front Immunol Immunology An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life. Frontiers Media S.A. 2021-05-03 /pmc/articles/PMC8128142/ /pubmed/34012452 http://dx.doi.org/10.3389/fimmu.2021.669811 Text en Copyright © 2021 Voigt, Inojosa, Dillenseger, Haase, Akgün and Ziemssen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Voigt, Isabel Inojosa, Hernan Dillenseger, Anja Haase, Rocco Akgün, Katja Ziemssen, Tjalf Digital Twins for Multiple Sclerosis |
title | Digital Twins for Multiple Sclerosis |
title_full | Digital Twins for Multiple Sclerosis |
title_fullStr | Digital Twins for Multiple Sclerosis |
title_full_unstemmed | Digital Twins for Multiple Sclerosis |
title_short | Digital Twins for Multiple Sclerosis |
title_sort | digital twins for multiple sclerosis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128142/ https://www.ncbi.nlm.nih.gov/pubmed/34012452 http://dx.doi.org/10.3389/fimmu.2021.669811 |
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