降低阿尔茨海默病识别的推断成本,使用单模和多模学习者的不确定性意识组合
Misgina Tsighe Hagos1,2, Kathleen M Curran3,4, Brian Mac Namee3,5
1Science Foundation Ireland Centre for Research Training in Machine Learning, University College Dublin, Dublin, D04 V1W8, Ireland. tsighemisgina@gmail.com.
Scientific reports
|February 14, 2025
概括
这项研究引入了一种用于阿尔茨海默病诊断的新深度学习方法,该方法可将昂贵且暴露于辐射的FDG PET扫描的需求降低高达92%,提高效率和患者安全.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 使用MRI和FDG PET的多模式深度学习显示出对阿尔茨海默病 (AD) 诊断的前景.
- 临床使用受限于在推断过程中对两种成像方式的要求.
- 诊断测试的选择往往取决于可用的信息和临床背景.
研究的目的:
- 开发一种新的多模式深度学习方法来诊断AD,模仿临床决策.
- 通过根据MRI不确定性估计来确定其必要性,减少对FDG PET扫描的依赖.
- 优化资源利用,提高阿尔茨海默病诊断中的患者安全.
主要方法:
- 提出了一种基于MRI和FDG PET的多模式深度学习方法.
- 从基于MRI的模型 (使用蒙特卡洛学和证据深度学习) 中纳入不确定性估计.
- 确定FDG PET扫描的必要性,只在多模式模型要求时输入它们.
主要成果:
- 显著减少了对FDG PET扫描的需求,高达92%.
- 在不影响诊断准确性的情况下保持模型性能.
- 确定了影响FDG PET必要性的关键大脑区域 (脑内皮层,杏仁体,心室).
结论:
- 拟议的方法有效地模仿了AD诊断的临床决策.
- 在FDG PET扫描使用量大幅减少,优化了资源配置和患者安全.
- 模型解释性为AD神经解剖学和诊断途径提供了临床相关的见解.
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