基于机器学习的算法用于虚拟早期检测和查神经退行性和神经认知障碍:一个系统性审查
Milad Yousefi1, Matin Akhbari2, Zhina Mohamadi3
1Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
Frontiers in neurology
|December 24, 2024
概括
机器学习 (ML) 显著改善了神经退行性和神经认知障碍的早期检测,如阿尔茨海默氏症和帕金森氏症,为患者的护理和管理提供了变革性的方法.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 数字健康数字健康
背景情况:
- 神经退行性和神经认知障碍会导致神经元损失和认知能力下降.
- 早期检测对于有效管理这些疾病至关重要.
- 传统的诊断方法在早期识别方面存在局限性.
研究的目的:
- 在神经退行性和神经认知障碍的早期检测中对机器学习 (ML) 的应用进行全面的系统审查.
- 评估ML如何可以提高诊断精度超越传统方法.
主要方法:
- 在PubMed,Scopus,Google Scholar和Web of Science的系统文献搜索,截至2023年8月15日.
- 使用RAYYAN工具和盲目审稿人的研究选和选择.
- 使用Cochrane偏差风险工具进行质量评估.
主要成果:
- 在选了7069项研究后,包括了108篇文章.
- 多个ML算法显示出早期发现痴呆症,帕金森病和轻度认知障碍的前景.
- ML对于成功检测阿尔茨海默病至关重要,而LASSO逻辑回归是轻度认知障碍的例外.
结论:
- 有必要将验证的数字健康资源,特别是ML,整合到标准医疗实践中.
- 机理学习的整合预示着神经退行性和神经认知性疾病的早期检测和管理的新时代.
- 在ML的进步提供了显著的潜力,以改善这些条件的全球健康结果.
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