人工智能在婴儿运动分类中的应用:对已满期和未满期婴儿的可靠性和有效性研究
Shiang-Chin Lin1, Erick Chandra2, Po Nien Tsao3
1School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan.
Physical therapy
|January 20, 2024
概括
这项研究开发了一种人工智能 (AI) 模型,以准确分类婴儿的运动. 人工智能模型对评估早产和早产婴儿的神经运动发育有希望.
科学领域:
- 生物医学工程 生物医学工程
- 发育儿科 发育儿科
- 人工智能的人工智能
背景情况:
- 过早出生的婴儿面临神经运动障碍的更高风险.
- 人工智能和数字技术的进步允许详细分析人类运动.
- 专注于成人的人工智能运动模型对婴儿评估的适用性尚未得到充分证实.
研究的目的:
- 开发和验证用于识别婴儿运动的AI模型框架.
- 评估人工智能驱动的婴儿运动评估的准确性.
- 评估该模型在区分满期婴儿和早产婴儿运动方面的有效性.
主要方法:
- 一项观察性研究涉及30名满期和54名4-18个月龄的早产婴儿.
- 在阿尔伯塔婴儿运动量表评估期间,使用5台同步视频摄像头收集的运动数据.
- 开发并测试了一种包含17点姿势估计和基于骨架的动作识别模型的AI算法.
主要成果:
- 153个评估会议产生了13139个婴儿运动视频.
- 在手动视频注释中具有高的intra-和interrater可靠性 (88%-100%).
- 人工智能算法在分类31个关键婴儿运动时实现了高精度 (0.91),回忆 (0.91),精度 (0.91) 和F1得分 (0.91).
结论:
- 开发的AI算法在临床环境中准确地分类了31个婴儿运动.
- 这种人工智能框架为使用家庭视频进行远程婴儿运动评估提供了基础.
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