多模式诊断工具和先进的数据模型用于检测先发性帕金森病:一个范围审查
Ibrahim Serag1, Ahmed Y Azzam2, Amr K Hassan3,4
1Faculty of Medicine, Mansoura University, Mansoura, Egypt. ibrahimserag@std.mans.edu.eg.
BMC medical imaging
|March 29, 2025
概括
早期发现前发性帕金森病 (PD) 是一个挑战. 多式成像和人工智能显示出确定早期PD的希望,但需要进一步验证临床使用.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 是一种进展性神经退行性疾病.
- 诊断依赖于运动症状,但发现前性PD具有挑战性.
- 在典型的运动症状之前出现了prodromal PD.
研究的目的:
- 为了探索目前的诊断方式,用于prodromal PD.
- 专注于多式联络成像和基于AI的方法.
主要方法:
- 根据PRISMA-SR指南进行范围审查.
- 在主要数据库 (PubMed,Scopus,Web of Science,Cochrane) 进行全面的文献搜索.
- 从9项包含的研究中提取数据 (567名前期PD患者,35,643名对照).
主要成果:
- 使用了包括神经成像和AI模型在内的各种诊断方法.
- 灵敏度在43%-84%之间,特异性高达96%.
- 神经黑色素敏感成像显示出最高的特异性;心电图+机器学习显示出最高的敏感性.
结论:
- 人工智能和多式神经成像显示出早期前性PD检测的前途.
- 由于缺乏验证,临床应用受到限制.
- 未来的研究应该专注于用于诊断和查的多式联络成像.
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