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相关实验视频

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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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基于移动设备的视频查婴儿头部滞后:一项探索性研究

Hao-Wei Chung1,2,3, Che-Kuei Chang4, Tzu-Hsiu Huang5

  • 1Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

Children (Basel, Switzerland)
|July 29, 2023
PubMed
概括

这项研究开发了一个人工智能 (AI) 模型,使用手机视频来评估婴儿的运动技能,为早期发现发育迟缓提供了一个新的工具.

关键词:
人工智能的人工智能是人工智能.婴儿头部滞后的时间发动机发育延迟是发动机发育延迟的一个原因.远程显示屏远程显示屏智能手机的智能手机智能手机的智能手机.

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科学领域:

  • 儿科神经学 儿科神经学
  • 医疗保健中的人工智能
  • 发育儿科 发育儿科

背景情况:

  • 基于视频的运动分析用于识别婴儿运动发育延迟.
  • 实验室记录图像和培训数据集的局限性阻碍了当前的方法.
  • 这项研究通过使用手机视频开发AI模型来解决这些局限性.

研究的目的:

  • 开发一种人工智能 (AI) 模型,用手机视频评估婴儿运动技能.
  • 克服传统的基于实验室的评估和有限的数据集的限制.
  • 为了使婴儿发育的可访问和远程监测.

主要方法:

  • 利用了父母在移动设备上录制的来自41名高危婴儿的270个视频.
  • 使用全身姿势估计和基于模糊的方法进行3D转换.
  • 使用全身骨架和关键点向量与领域知识训练AI模型,评估Pull to Sit (PTS) 级别.

主要成果:

  • 关键点模型在0级婴儿运动技能方面取得了88.062%的准确性.
  • 全身骨模型在0.0级的准确率为77.667%.
  • 3级ROC曲线下的面积 (AUC) 达到96.049% (骨架) 和94.333% (关键点).

结论:

  • 具有最小环境限制的AI模型可以促进以家庭为中心的发育迟缓查.
  • 这种方法可以对需要早期干预的婴儿进行远程监测.
  • 人工智能模型为可访问的婴儿运动技能评估提供了一个有希望的解决方案.