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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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牙正治疗结果预测性能 人工智能和传统方法之间的差异.

Sung Joo Cho, Jun-Ho Moon, Dong-Yub Ko

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    此摘要是机器生成的。

    人工智能 (AI) 模型被评估为预测正治疗结果,发现传统方法总体上更准确. 然而,人工智能在预测软组织变化方面表现出色,这表明混合方法可能是最佳的.

    关键词:
    人工智能的人工智能是人工智能.机器学习是机器学习.多变量多重线性回归.ортодонтическое 治疗 ортодонтическое 治疗部分最小正方形.变更个人资料 变更个人资料

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    科学领域:

    • 矯正牙科 矯正牙科是一種矯正牙科.
    • 医疗成像医学成像
    • 人工智能的人工智能

    背景情况:

    • 在治疗计划中,预测牙矯正后软组织和大气泡骨的治疗变化至关重要.
    • 传统的统计模型在捕捉复杂的形态变化方面存在局限性.

    研究的目的:

    • 评估人工智能模型在 ортодонтической治疗后对软组织和膜骨变化的预测准确度.
    • 将AI模型的性能与传统的统计预测方法进行比较.

    主要方法:

    • 分析了来自887名成年正牙患者的1774张横向头脑图.
    • 人工智能 (TabNet) 和传统方法 (MMLR,PLSR) 用于预测44个硬和软组织标志.
    • 预测变量包括人口统计,临床数据和地标坐标.

    主要成果:

    • 多变量多重线性回归 (MMLR) 显示出最高的预测准确性.
    • 人工智能模型的整体准确性最低,但在5个特定的软组织里程碑中表现优于常规方法,低于胸膜.
    • 人工智能对软组织里程碑具有卓越的预测能力,具有显著的可变性.

    结论:

    • 目前,人工智能模型在整体预测牙科治疗变化方面效率低于传统的统计方法.
    • 人工智能显示出预测特定软组织变化的前景.
    • 结合人工智能和传统方法的混合模型可能会提供更好的预测能力.