Lesport Quentin, Palmie Davis, Öztosun Gülşen, Kaminski Henry J, Garbey Marc
Care Constitution Corp., Newark, DE 19702, USA.
Laboratoire des Sciences de l'Ingénieur pour l'Environnement (LaSIE) UMR-CNRS 7356, University of La Rochelle, 17000 La Rochelle, France.
Bioengineering (Basel). 2024 Sep 20;11(9):942. doi: 10.3390/bioengineering11090942.
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.
远程医疗目前正更频繁地用于评估重症肌无力（MG）患者。评估这种疾病涉及临床结局指标，如标准化的MG-ADL量表或临床检查期间获得的更复杂的MG-CE评分。然而，人为主观性限制了这些检查的可靠性。我们提出了一套由人工智能驱动的数字工具，利用计算机视觉、深度学习和自然语言处理来提高评分效率和质量。本文重点通过将标准远程医疗视频分割成与MG-CE评估相对应的片段来实现其自动化。这种由人工智能驱动的解决方案提供了对神经功能缺损的定量评估，改进了容易因检查者差异而产生的主观评估。它有可能提高效率、增加患者对MG临床试验的参与度，并更广泛地应用于各种神经系统疾病。
