基于神经成像的阿尔茨海默病预测利用电子健康中的深度学习方法的最新进展:系统性审查
Zia-Ur-Rehman1, Mohd Khalid Awang1, Ghulam Ali2
1Faculty of Informatics and Computing (FIK) Universiti Sultan Zainal Abidin (UniSZA) Besut Terengganu Malaysia.
Health science reports
|May 7, 2025
概括
通过神经成像进行深度学习 (DL),显著改善了阿尔茨海默病 (AD) 的诊断. 虽然挑战仍然存在,但人工智能的进步提供了有希望的早期检测和更好的患者护理.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病 (AD) 诊断与传统方法具有挑战性,通常错过了早期阶段.
- 与深度学习 (DL) 集成的神经成像显示了早期和准确的AD检测的希望.
- 早期发现AD对于有效治疗,降低死亡率和医疗成本至关重要.
研究的目的:
- 审查当前深度学习 (DL) 方法的发展,使用神经影像用于阿尔茨海默病 (AD) 诊断.
- 检查流行的神经成像技术,可访问的数据集和DL算法用于AD评估.
- 提供关于人工智能驱动的AD诊断最新进展的见解.
主要方法:
- 在主要科学数据库中进行广泛的文献搜索 (2021-2025).
- 专注于DL模型应用于MRI,PET和fMRI等神经成像模式.
- 遵守系统审查和元分析 (PRISMA) 准则的首选报告项目.
主要成果:
- 基于卷积神经网络 (CNN) 的DL,特别是混合和转移学习,表现出卓越的表现.
- 多模式神经成像数据集成提高了诊断准确度.
- 关键的挑战包括方法解释性,数据异质性和数据稀缺性.
结论:
- 深度学习通过神经成像显著提高了AD诊断的准确性和可靠性.
- 解决数据可访问性和可解释性问题对于临床翻译至关重要.
- 未来的研究应该专注于标准化数据集,强大的验证和可解释的AI用于AD预测.
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