Work

AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations

Year: 2024

Type: article

Abstract: 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.

Source: Bioengineering

DOI: 10.3390/bioengineering11090942

Authors: Quentin Lesport, Davis Palmie, Gülşen Öztosun, Henry J. Kaminski, Marc Garbey

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences de l'Ingénieur pour l'Environnement, CancerCare, George Washington University

Language: English

Topic: Telemedicine and Telehealth Implementation

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,069