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通过人工智能和机器学习推进脊柱护理:概述和应用

Andrea Cina^1,2, Fabio Galbusera¹

¹Spine Center, Schulthess Clinic, Zurich, Switzerland.

EFORT open reviews

|May 10, 2024

概括

机器学习 (ML) 是人工智能的一个子集,通过改善诊断和治疗选择来增强脊柱护理. 这项技术分析放射图像和各种数据,以实现个性化医疗和脊柱研究中更好的患者结果.

科学领域:

脊柱护理和研究
人工智能的人工智能是人工智能.
机器学习是机器学习.

背景情况:

机器学习 (ML) 对于推进脊柱护理和研究至关重要.
它利用广泛的医疗保健数据来改善诊断,决策支持和治疗选择.
ML的功能在分析脊柱状况的放射图像方面尤为重要.

研究的目的:

探索机器学习在脊柱护理和研究中的应用.
详细介绍各种ML技术及其与脊柱数据分析的相关性.
强调在临床环境中验证ML模型的重要性.

主要方法:

讨论监督和无监督学习,回归,分类和集群.
关于ML算法的概述,包括线性模型,支向量机器,决策树,神经网络和深度卷积神经网络.
对各种数据类型的分析:视觉,表格,omics和多式联络.

主要成果:

ML有助于放射性图像分析,包括定位,标记和发现的检测.
机器学习模型可以预测临床结果,支持个性化医疗方法.
内部和外部验证对于评估ML模型性能和可靠性至关重要.

结论:

机器学习具有巨大的潜力,可以通过增强的诊断和个性化治疗策略来彻底改变脊柱护理.
在各种数据类型中应用各种ML算法可以导致更准确和更有效的脊柱护理.
严格验证ML模型对于其在临床实践和研究中的成功实施至关重要.

关键词:

计算机辅助诊断是指计算机辅助的诊断.决策支持提供了决策支持.深度学习是一种深度学习.结果预测结果预测.处理算法处理算法

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