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基于深度学习的行为识别技术用于儿科行为听力测量.

Wen Xie1, Chunhua Li1, Haisen Peng1

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概括
此摘要是机器生成的。

这项研究介绍了用于儿科听力测试的AI驱动系统,利用深度学习进行姿势识别以自动化评估. 虽然人工智能显示出希望,但人类评估在4-6岁儿童的特异性和AUC方面仍然优越.

关键词:
在这里,我们可以看到AIAIAI.图像处理 图像处理儿科行为听力学.儿科听力损失 儿科听力损失骨架关键点的关键点变压器 变压器 变压器

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科学领域:

  • 听力学 听力学是指听力学.
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 儿科行为听力测量对于早期听力损失检测至关重要.
  • 传统的方法可能是主观和耗时的.
  • 需要客观和自动化的评估工具来提高准确性和效率.

研究的目的:

  • 开发和验证一个基于深度学习的系统,用于自动化儿科行为听力测量.
  • 评估人工智能驱动的姿势识别在评估儿童听力方面的可行性和准确性.
  • 建立基于运动分析的客观听力评估的决策规则.

主要方法:

  • 从行为听力测试视频创建一个专门的儿科姿势检测数据集.
  • 使用优化变压器开发智能诊断模型 (DoT) 和骨架关键点估计模型 (POTR).
  • 实施姿势识别,实时监测和分析儿童在听力测试中的动作.
  • 建立听力学知情决策规则,用于听力水平评估.

主要成果:

  • 在2.5-4岁的儿童中,人工智能听力测量显示出比人工方法更高的灵敏度 (0.929比0.900).
  • 人工听力测量在2.5-4年组中显示出更高的特异性 (0.824对AI) 和AUC (0.901对AI).
  • 在4-6岁儿童中,人工听力测量在灵敏度 (0.943),特异性 (0.947) 和AUC (0.924) 方面优于人工智能.

结论:

  • 基于深度学习的姿势识别为自动化儿科听力评估提供了一种可行的方法.
  • 人工智能驱动的听力测量显示了提高幼儿灵敏度的潜力.
  • 需要进一步细化,以匹配或超过传统方法的特异性和AUC,特别是在老年儿童中.
  • 开发的系统为客观诊断和儿童听力障碍早期干预提供了基础.