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阿尔茨海默病风险预测的未来:系统性审查
1Nanomaterials & Devices (NMD) Laboratory, School of Computing, Electronics and Mathematics , Plymouth University, Devon, PL4 8 AA, UK. sophia.nazir@plymouth.ac.uk.
概括
机器学习和人工智能在使用各种数据预测阿尔茨海默病风险方面非常有前途. 这些先进的方法可以改善早期诊断和痴呆的个性化干预.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 阿尔茨海默病是全球老年人痴呆的主要原因.
- 传统的风险预测依赖于人口统计和临床数据.
- 机器学习 (ML) 和人工智能 (AI) 使用医学成像和报告提供个性化的风险模型.
研究的目的:
- 整合传统和基于人工智能的模型来预测阿尔茨海默病 (AD).
- 分析神经成像和非神经成像特征 (成像,放射学,遗传学,临床数据).
- 为研究人员提供ML的全面概述,用于AD风险预测.
主要方法:
- 对阿尔茨海默病风险评估中的ML研究的系统审查.
- 对磁共振成像 (MRI),遗传学,放射学和医学数据的分析.
- 包括2000年至2024年间发表的120项研究.
主要成果:
- 深度学习 (DL) 和ML是诊断神经系统疾病的核心.
- 在神经成像和非神经成像数据上应用的ML模型为研究人员和临床医生提供了信息.
- 这篇评论提供了一个关于AI在阿尔茨海默病风险评估中的广泛指南.
结论:
- 整合多种数据源可以提高人工智能在阿尔茨海默病风险预测中的应用.
- 人工智能对改善阿尔茨海默氏症的早期诊断具有重大潜力.
- 个性化干预策略可以通过人工智能驱动的风险评估来推进.
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