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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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可解释的人工智能对于梅奥内镜评分在性结肠炎.

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    可解释AI (XAI) 模型显著改善了梅奥内镜评分 (MES) 在性结肠炎 (UC) 的评估. 在解释UC严重程度方面,XAI提高了AI性能和内镜师的准确性.

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    科学领域:

    • 胃肠病学 胃肠病学
    • 人工智能的人工智能
    • 医疗成像医学成像

    背景情况:

    • 梅奥内镜评分 (MES) 对于评估性结肠炎 (UC) 严重程度和指导治疗至关重要.
    • 目前用于MES的深度学习 (DL) 模型缺乏透明度,阻碍了临床信任和采用.
    • 可解释性AI (XAI) 提供了一种解决方案,可以提高医学诊断中DL模型的可解释性.

    研究的目的:

    • 开发和评估可解释的AI (XAI) 模型,用于确定UC患者的MES.
    • 将XAI模型的性能与传统DL模型和人类内镜师的性能进行比较.
    • 调查人工智能协助对MES解释中的内镜师表现的影响.

    主要方法:

    • 一项回顾性多中心研究使用了2600张白光内镜图像来训练DL模型.
    • 采用了两种方法:传统的黑子模型和可解释的AI (XAI) 模型.
    • 模型在三个外部数据集上得到验证,并与内镜评估进行比较,使用Grad-CAM进行视觉解释.

    主要成果:

    • 一个支持Xception的XAI模型在外部数据集上实现了高精度 (0.910) 和相关系数,优于其他模型和内镜师.
    • 人工智能辅助显著改善了内镜师的性能,使高级内镜师的精度从0.890增加到0.930,初级内镜师的精度从0.810增加到0.880.
    • 在所有三个外部测试数据集中都观察到类似的性能改进,证实了模型的稳定性.

    结论:

    • 采用可解释的AI框架可以提高DL模型的性能和UCMES评估中的透明度.
    • XAI工具可以增强内镜师的能力,从而更准确,更可靠地解释UC内镜检测结果.
    • 整合XAI有望改善性结肠炎管理中的临床决策和患者结果.