多模式融合和人工智能模型在阿尔茨海默病检测中的可解释性
Vimbi Viswan1, Noushath Shaffi2, E Malathy3
1Department of Computing and Information Sciences, University of Technology and Applied Sciences, Jamia Street, 311, Suhar, Sultanate of Oman.
Brain informatics
|February 2, 2026
概括
将各种数据类型与人工智能 (AI) 集成,可以改善阿尔茨海默病 (AD) 的预测. 本综述综合了AD的AI多模式融合,强调可解释性和更好的诊断模型的严格方法.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 阿尔茨海默病 (AD) 诊断和预后受益于人工智能中的多式联络数据集成.
- 当前的人工智能模型在结合各种数据源时,往往缺乏可解释性和方法严格性.
研究的目的:
- 系统地审查和综合最近在人工智能驱动的多式联络融合的进展,以预测AD.
- 分析数据集,预处理技术和融合策略,以提高诊断准确度.
- 评估可解释AI (XAI) 在多式联络AI研究中的整合.
主要方法:
- 系统的文献审查,遵循PRISMA和基奇纳姆的指导方针.
- 数据集的分类,预处理方法 (神经成像,电生理学,基因组学) 和融合策略 (数据级,特征级,决策级,时间级).
- 分析可解释AI (XAI) 技术的采用和影响.
主要成果:
- 多模式数据集成显著提高AI模型的准确性和可解释性,用于AD预测.
- 存在多种不同的预处理和融合策略,对诊断性能产生不同的影响.
- 结合多模式,可解释性和方法严谨性的研究不足.
结论:
- 对AD的强大,可解释的AI模型需要仔细考虑多式联运数据集成和可解释性.
- 未来的研究应该专注于为AD开发标准化,透明和临床相关的AI框架.
- 解决结合多模式性和可解释性的研究差距对于临床翻译至关重要.
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