Literature DB >> 33763475

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis.

Ying Zhang1, Hongmei Yu1, Rui Dong1, Xuan Ji1, Fujun Li2.   

Abstract

Myasthenia gravis (MG) is a chronic autoimmune disease of the nervous system, which is still incurable. In recent years, with the progress of immunosuppressive and supportive treatment, the therapeutic effect of MG in the acute stage is satisfactory, and the mortality rate has been greatly reduced. However, there is still no consensus on how to conduct long-term management of stable MG, such as guiding patients to identify relapses, practice exercise, return to work and school, etc. In the international consensus guidance for management of myasthenia gravis published by the Myasthenia Gravis Foundation of America (MGFA) in 2020, for the first time, "the role of physical training/exercise in MG" was identified as the topic of discussion. Finally, due to a lack of high-quality evidence on physical training/exercise in patients with MG, the topic was excluded after the literature review. Therefore, this paper reviewed the current status of MG rehabilitation research and the difficulties faced by stable MG patients in self-management. It is suggested that we should take advantage of artificial intelligence (AI) and leverage it to develop the data-driven decision support platforms for MG management which can be used for adverse event monitoring, disease education, chronic management, and a wide variety of data collection and analysis.
Copyright © 2021 Ying Zhang et al.

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Year:  2021        PMID: 33763475      PMCID: PMC7952150          DOI: 10.1155/2021/5592472

Source DB:  PubMed          Journal:  Biomed Res Int            Impact factor:   3.411


  51 in total

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Review 10.  International Consensus Guidance for Management of Myasthenia Gravis: 2020 Update.

Authors:  Pushpa Narayanaswami; Donald B Sanders; Gil Wolfe; Michael Benatar; Gabriel Cea; Amelia Evoli; Nils Erik Gilhus; Isabel Illa; Nancy L Kuntz; Janice Massey; Arthur Melms; Hiroyuki Murai; Michael Nicolle; Jacqueline Palace; David Richman; Jan Verschuuren
Journal:  Neurology       Date:  2020-11-03       Impact factor: 9.910

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