AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations

Document Type

Journal Article

Publication Date

9-20-2024

Journal

Bioengineering (Basel, Switzerland)

Volume

11

Issue

9

DOI

10.3390/bioengineering11090942

Keywords

clinical trial; computer vision; deep learning; diplopia; eye-tracking; myasthenia gravis; neurological disease; ptosis; telehealth; telemedicine

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.

Department

Surgery

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