基于视频的帕金森病评估中的手跟踪深度学习:当前和未来的观点
Gianluca Amprimo1, Giulia Masi2, Gabriella Olmo3
1Politecnico di Torino - Control and Computer Engineering Department, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy; National Research Council - Institute of Electronics, Information Engineering and Telecommunications, Corso Duca degli Abruzzi, 24, Turin, 10029, Italy.
Artificial intelligence in medicine
|June 23, 2024
概括
深度学习手跟踪从视频中提供客观的帕金森病评估. 本综述探讨了早期诊断和监测的当前框架和未来方向.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 帕金森病 (PD) 需要早期诊断和症状评估.
- 手动分析对于了解PD进展至关重要.
- 深度学习 (DL) 的进步可以通过视频分析运动任务实现客观的,自动化的PD评估.
研究的目的:
- 审查基于视频的PD评估中用于手部跟踪的最新DL框架.
- 概述这一领域的未来前景.
主要方法:
- 在主要数据库 (PubMed,科学网,IEEE Explorer,Scopus) 进行了系统的文献搜索,直到2023年10月.
- 关键词包括:帕金森,手跟踪,深度学习,基于视频的评估.
- 符合标准的23个出版物被选中进行审查.
主要成果:
- 选择的研究使用了像OpenPose,MediaPipe这样的框架,以及用于手/手指跟踪的自定义DL架构.
- 用手跟踪数据将PD患者与对照患者区分开来.
- 提取的特征有助于表征症状 (震,勃拉迪基尼西亚) 和评估临床尺度 (MDS-UPDRS).
结论:
- 用DL驱动的手跟踪显示了精确,客观的PD评估和监测的潜力,特别是在远程医疗中.
- 标准化和验证对于临床接受至关重要.
- 未来的研究应该集中在大型数据集,患者验证和评估日常任务中的手动交互.
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