Abstract
Original language | English |
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Pages (from-to) | 989-999 |
Number of pages | 11 |
Journal | Neurology |
Volume | 97 |
Issue number | 21 |
DOIs | |
Publication status | Published - 23 Nov 2021 |
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In: Neurology, Vol. 97, No. 21, 23.11.2021, p. 989-999.
Research output: Contribution to journal › Review article › Academic › peer-review
TY - JOUR
T1 - Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence
AU - MAGNIMS Study Group
AU - Vrenken, Hugo
AU - Jenkinson, Mark
AU - Pham, Dzung L.
AU - Guttmann, Charles R. G.
AU - Pareto, Deborah
AU - Paardekooper, Michel
AU - de Sitter, Alexandra
AU - Rocca, Maria A.
AU - Wottschel, Viktor
AU - Cardoso, M. Jorge
AU - Barkhof, Frederik
N1 - Funding Information: H. Vrenken has received funding support from the Dutch MS Research Foundation (grant 14-876 MS), ZonMW jointly with the Dutch MS Research Foundation (grant 40-44600-98-326), and HealthHolland (grant LSHM19053). The MS Center Amsterdam is supported by the Dutch MS Research Foundation through a series of program grants (current grant 18-358f). M. Jenkinson is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and this research was funded by the Wellcome Trust (215573/Z/19/Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). D.L. Pham received funding support from National Multiple Sclerosis Society Grants RG-1507-05243 and RG-1907-34570, Congressionally Directed Medical Research Programs Grant W81XWH-20-1-0912, and the Department of Defense in the Center for Neuroscience and Regenerative Medicine. C.R.G. Guttmann acknowledges support from the National Multiple Sclerosis Society (grant identifier RG-1501-03141), the International Progressive Multiple Sclerosis Alliance (grant identifier PA-1412-02420), the Foundation of the University of Bordeaux, Roche Pharmaceuticals, and Talan. D. Pareto received support from Instituto de Salud Carlos III (PI18/00823). V. Wottschel has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 666992. M.J. Cardoso is supported by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z) and the Wellcome Flagship Programme (WT213038/Z/18/Z). F. Barkhof is supported by the NIHR biomedical research center at UCLH. Funding Information: The Article Processing Charge was funded by Vrije Universiteit, Amsterdam, the Netherlands. Funding Information: H. Vrenken has received funding support from the Dutch MS Research Foundation (grant 14-876 MS), ZonMW jointly with the Dutch MS Research Foundation (grant 40-44600-98-326), and HealthHolland (grant LSHM19053). The MS Center Amsterdam is supported by the Dutch MS Research Foundation through a series of program grants (current grant 18-358f). M. Jenkinson is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and this research was funded by the Wellcome Trust (215573/Z/19/Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). D.L. Pham received funding support from National Multiple Sclerosis Society Grants RG-1507-05243 and RG-1907-34570, Congressionally Directed Medical Research Programs Grant W81XWH-20-1-0912, and the Department of Defense in the Center for Neuroscience and Regenerative Medicine. C.R.G. Guttmann acknowledges support from the National Multiple Sclerosis Society (grant identifier RG-1501-03141), the International Progressive Multiple Sclerosis Alliance (grant identifier PA-1412-02420), the Foundation of the University of Bordeaux, Roche Pharmaceuticals, and Talan. D. Pareto received support from Instituto de Salud Carlos III (PI18/00823). V. Wottschel has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement 666992. M.J. Cardoso is supported by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z) and the Wellcome Flagship Programme (WT213038/Z/18/Z). F. Barkhof is supported by the NIHR biomedical research center at UCLH. Funding Information: This article is based on a workshop of the MAGNIMS Study Group that was made possible through financial support from the Dutch MS Research Foundation, Amsterdam Neuroscience, VU University Medical Center, Merck KGaA, and Novartis. Publisher Copyright: Copyright © 2021 The Author(s).
PY - 2021/11/23
Y1 - 2021/11/23
N2 - Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.
AB - Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.
UR - http://www.scopus.com/inward/record.url?scp=85122111693&partnerID=8YFLogxK
U2 - https://doi.org/10.1212/WNL.0000000000012884
DO - https://doi.org/10.1212/WNL.0000000000012884
M3 - Review article
C2 - 34607924
SN - 0028-3878
VL - 97
SP - 989
EP - 999
JO - Neurology
JF - Neurology
IS - 21
ER -