AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations
Telemedicine is now being used more frequently to evaluate patients with myasthenia gravis (MG). Assessing this condition involves clinical outcome measures, such as the standardized MG-ADL scale or the more complex MG-CE score obtained during clinical exams. However, human subjectivity limits the reliability of these examinations. We propose a set of AI-powered digital tools to improve scoring efficiency and quality using computer vision, deep learning, and natural language processing. This paper focuses on automating a standard telemedicine video by segmenting it into clips corresponding to the MG-CE assessment. This AI-powered solution offers a quantitative assessment of neurological deficits, improving upon subjective evaluations prone to examiner variability. It has the potential to enhance efficiency, patient participation in MG clinical trials, and broader applicability to various neurological diseases.
Authors
- Gülşen Öztosun (ORCID: https://orcid.org/0000-0002-1638-9095)
- Quentin Lesport
- Henry J. Kaminski (ORCID: https://orcid.org/0000-0002-8195-0141)
- Marc Garbey (ORCID: https://orcid.org/0000-0001-7898-9136)
- Davis Palmie
Institutions
- Centre National de la Recherche Scientifique (FR)
- George Washington University (US)
- Laboratoire des Sciences de l'Ingénieur pour l'Environnement (FR)
- La Rochelle Université (FR)
Publication Details
- Journal
- Bioengineering
- Published
- 2024-09-20
- DOI
- https://doi.org/10.3390/bioengineering11090942
- Citations
- 6
- Primary Topic
- Myasthenia Gravis and Thymoma
- Type
- article
- Field-Weighted Citation Impact
- 2.21
Funders
- National Science Foundation
- George Washington University
- Rare Diseases Clinical Research Network
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke
- National Center for Advancing Translational Sciences