床边超声波以识别和预测缺血性中风后的缺食症的严重程度:人类与人工智能对比
Keith Barron1, Michael Blaivas2, Laura Blaivas3
1Prisma Health Midlands/Department of Internal Medicine, University of South Carolina School of Medicine, Columbia, SC, USA.
Ultrasound in medicine & biology
|October 20, 2023
概括
一个机器学习算法使用超声波准确地预测了中风患者的吞困难,为食障碍评估提供了一个有希望的,易于使用的工具. 这种方法与标准测试有很大的一致性,改善了患者的护理.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 缺血症是缺血性中风后的常见和严重并发症,增加了吸入性肺炎的风险.
- 目前用于食障碍评估的方法,如视频光镜 (VFS) 和纤维光学内镜吞评估 (FEES),可能在后勤上具有挑战性和资源密集.
- 需要客观和可访问的方法来识别风险中风患者,以改善失足症的管理.
研究的目的:
- 为了研究定量超声波评估的状骨运动在预测吞障碍在急性缺血性中风患者的实用性.
- 为了评估机器学习 (ML) 算法的性能,从实时超声波视频录制的吞中预测透-吸收量表 (PAS) 的结果.
主要方法:
- 一项前性,单盲观察试点研究,涉及69名接受VFS或FEES的急性缺血性中风患者.
- 中气道超声波在吞过程中进行,语音语言病理学家对超声波结果视而不见.
- 开发了一种机器学习算法,利用长期短期记忆网络与VGG16卷积神经网络,用于实时视频分析状骨运动.
主要成果:
- 手动超声波测量状骨运动 (升高,向前位移,总位移) 并没有显著预测PAS评估结果.
- 开发的ML算法与PAS测试结果在预测吞评估结果方面表现出相当大的一致性 (科恩的 κ = 0.79).
- 机器学习算法在预测PAS成功或失败方面取得了很高的准确性,即使使用相对较小的数据集.
结论:
- 单独对状骨运动的定量超声波测量不足以预测中风患者的失足症严重程度.
- 应用在超声视频上的机器学习算法显示了对中风幸存者的精确和可访问的消化障碍评估的巨大潜力.
- 这种以ML驱动的超声波方法可以提高临床环境中缺食症评估的效率和可用性.
相关概念视频
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