从1992年到2022年进行的步态分析和人工智能研究的全球图书统计和可视化分析
Tong Bao1,2,3, Jiasi Gao4, Jinyi Wang1,3
1School of Medicine, Tsinghua University, Beijing, China.
Frontiers in robotics and AI
|December 4, 2023
概括
人工智能 (AI) 通过提高准确性和效率来增强临床步态分析. 本研究审查了步行中的AI应用,重点关注机器学习和疾病管理的神经网络.
科学领域:
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
- 人工智能在医学中的应用
背景情况:
- 异常的步态显著影响生活质量,导致跌倒和运动能力下降.
- 步态分析,包括动力学,动力学和EMG,对于治疗规划至关重要.
- 人工智能 (AI) 为步态分析准确性和可访问性提供了变革性的潜力.
研究的目的:
- 系统地审查步态分析和人工智能应用的研究状态.
- 介绍AI在临床步态分析中的作用,用于疾病识别和管理.
- 评估用于步态分析的AI算法,并讨论未来的研究方向.
主要方法:
- 从WoS核心集合的步态分析和AI文章 (1992-2022) 的图书统计和视觉分析.
- 使用VosViewer软件进行网络可视化和关键字分析.
- 在已发表的研究中比较了不同AI算法的预测能力.
主要成果:
- 机器学习 (ML) 和人工神经网络 (ANN) 是步态分析中占主导地位的AI方法.
- 人工智能应用正在越来越多地影响跨疾病的步态异常的识别和管理.
- 该审查系统地绘制了步态分析中人工智能的研究格局.
结论:
- 人工智能显著增强了临床步态分析,改善了诊断和治疗结果.
- 需要进一步的研究来比较AI算法在不同临床场景中的性能.
- 本综述为研究人员和投资者提供了人工智能驱动的步态分析的宝贵见解.
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