计算机辅助决策支持系统的阿尔茨海默病诊断 - 一个系统的审查
Tuğba Günaydın1, Songül Varlı1
1Department of Computer Engineering, Yıldız Technical University, Istanbul, Türkiye.
Current medical imaging
|June 4, 2025
概括
计算机辅助系统在早期发现阿尔茨海默病方面表现有前途. 多模式数据和先进的机器学习提高了诊断准确性,但数据不平衡等挑战需要解决.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 神经科学是一个神经科学.
背景情况:
- 随着人口老龄化,阿尔茨海默病 (AD) 的发病率在全球范围内正在增加.
- 早期AD诊断对于减缓疾病进展至关重要,因为目前还没有治愈方法.
- 计算机辅助诊断系统是提高早期AD检测的重要工具.
研究的目的:
- 系统地审查最近在AD诊断的计算机辅助决策支持系统的进展.
- 在AD分类中评估数据模式,机器学习方法和性能指标.
- 确定该领域的当前趋势和局限性.
主要方法:
- 系统审查遵循系统审查和元分析 (PRISMA) 准则的首选报告项目.
- 在PubMed,IEEEEXplore和科学网络中从2021年至2024年进行文献搜索.
- 包括39项使用神经成像 (MRI,PET) 和机器学习的生物标志物用于AD分类的研究.
主要成果:
- 多模式方法 (MRI,PET,认知评估) 显示出比单一模式更高的诊断准确性和可靠性.
- 卷积神经网络是最普遍的机器学习模型,其次是混合和随机森林模型.
- 报告的准确度很高:对二进制的分类高达100%,对多类AD分类高达99.98%. 像合成少数人过量采样技术 (SMOTE) 和数据增强等技术解决了数据不平衡.
结论:
- 多模式数据集成显著提高了计算机辅助AD诊断的准确性.
- 关键的局限性包括数据不平衡,样本规模小,外部验证不足.
- 未来的研究应该专注于更大,多样化的数据集,纵向数据,现实世界的临床验证和临床信任的模型可解释性.
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