This paper is in the following e-collection/theme issue:

Neurotech Innovations, Diagnostic Tools and Techniques (39) New Methods (974) Telehealth and Telemonitoring (1744) Machine Learning (1535) Neurotechnology in Clinical Studies (15)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43387, first published .
A Digital Telehealth System to Compute Myasthenia Gravis Core Examination Metrics: Exploratory Cohort Study

A Digital Telehealth System to Compute Myasthenia Gravis Core Examination Metrics: Exploratory Cohort Study

A Digital Telehealth System to Compute Myasthenia Gravis Core Examination Metrics: Exploratory Cohort Study

Authors of this article:

Marc Garbey1, 2, 3, 4 Author Orcid Image ;   Guillaume Joerger2, 4 Author Orcid Image ;   Quentin Lesport1, 3, 4 Author Orcid Image ;   Helen Girma5 Author Orcid Image ;   Sienna McNett5 Author Orcid Image ;   Mohammad Abu-Rub5 Author Orcid Image ;   Henry Kaminski5 Author Orcid Image
Article Authors Cited by (4) Tweetations (8) Metrics

Journals

  1. Chang C, Liu T, Lu C, Chiu H, Lin W. Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis. Frontiers in Microbiology 2023;14 View
  2. Demuth S, Paris J, Faddeenkov I, De Sèze J, Gourraud P. Clinical applications of deep learning in neuroinflammatory diseases: A scoping review. Revue Neurologique 2024 View
  3. Lesport Q, Palmie D, Öztosun G, Kaminski H, Garbey M. AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations. Bioengineering 2024;11(9):942 View
  4. Garbey M, Lesport Q, Girma H, Öztosun G, Abu-Rub M, Guidon A, Juel V, Nowak R, Soliven B, Aban I, Kaminski H. Application of digital tools and artificial intelligence in the Myasthenia Gravis Core Examination. Frontiers in Neurology 2024;15 View