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相关概念视频

Nociception01:44

Nociception

28.4K
Nociception—the ability to feel pain—is essential for an organism’s survival and overall well-being. Noxious stimuli such as piercing pain from a sharp object, heat from an open flame, or contact with corrosive chemicals are first detected by sensory receptors, called nociceptors, located on nerve endings. Nociceptors express ion channels that convert noxious stimuli into electrical signals. When these signals reach the brain via sensory neurons, they are perceived as pain.
28.4K

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相关实验视频

Updated: May 6, 2026

Electrophysiological Measurements and Analysis of Nociception in Human Infants
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Electrophysiological Measurements and Analysis of Nociception in Human Infants

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探索基于视觉的新生儿疼痛评估中的注释偏差.

Yuxin Ni, Huaiyu Zhu, Yisheng Zhao

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    PubMed
    概括
    此摘要是机器生成的。

    新生儿疼痛评估工具可以通过ScorER改进,ScorER是一个新的AI系统,可以识别和纠正基于视觉的疼痛注释中的错误. 这有助于创建更好的人工智能来管理婴儿疼痛.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能的人工智能
    • 新生儿医学 新生儿医学

    背景情况:

    • 新生儿疼痛对大脑发育有负面影响,需要准确的疼痛评估.
    • 自动化疼痛评估系统至关重要,但面临数据收集和注释挑战.
    • 基于视觉的注释容易产生现实世界的干扰,导致数据错误.

    研究的目的:

    • 引入评分错误识别 (ScorER),这是一个深度神经网络工具,用于识别新生儿疼痛评估中的基于视觉的注释偏差.
    • 提高用于开发自动化新生儿疼痛评估系统的数据质量和可靠性.

    主要方法:

    • 开发了一个基于深度神经网络的工具ScorER.
    • 集成了Grad-CAM算法来识别与注释偏差相关的关键区域.
    • 利用数据驱动的方法来分析注释错误的原因.

    主要成果:

    • ScorER有效地识别和诊断基于视觉的新生儿疼痛注释中的偏差.
    • 在Grad-CAM集成中,确定了导致注释不准确的区域.
    • 该研究提供了一种方法来提高AI开发的注释质量.

    结论:

    • 斯科尔 (ScorER) 帮助护理人员制作新生儿疼痛的高质量注释.
    • 该工具支持为人工智能应用程序创建大规模可靠的临床数据库.
    • 促进人工智能驱动的自动化新生儿疼痛评估和管理的进步.