生物标志物 生物标志物
Taslim Murad1, Hui-Yuan Miao1, Deepa S Thakuri2
1Washington University in St. Louis, St. Louis, MO, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
这项研究引入了一种新的可解释的人工智能 (AI) 方法DL-SHAP,使用脑MRI扫描来预测阿尔茨海默病 (AD) 的认知衰退. 人工智能模型准确地识别了与认知功能和疾病严重程度相关的关键大脑区域.
科学领域:
- 神经成像和人工智能的人工智能
- 阿尔茨海默氏症疾病研究研究
- 大脑体积测量分析
背景情况:
- 通过MRI检测到的大脑体积变化与阿尔茨海默病 (AD) 的认知衰退相关.
- 之前使用单变量方法的研究探索了个体大脑区域与认知的关联.
- 整个大脑 (WB) 体积变化与阿尔茨海默氏症的认知之间的多变量关系仍未得到充分探索.
研究的目的:
- 通过使用可解释的人工智能,研究AD连续体中的复杂多变量WB认知关系.
- 从MRI数据中预测全球认知 (迷你心理状态检查 - MMSE) 使用WB区域特征.
- 为了确定对认知预测和AD严重程度 (临床痴呆症评分-盒子总和-CDR-SB) 有贡献的关键大脑区域.
主要方法:
- 使用机器学习 (ML) 和深度学习 (DL) 模型进行认知预测.
- 集成了一个最佳的DL模型与特征重要性 (DL-SHAP) 的Shapley添加式扩展 (SHAP).
- 在半模拟 (n=1108) 和实验 (n=668) MRI 数据集上验证了DL-SHAP,评估了MMSE和CDR-SB.
主要成果:
- 在MMSE预测方面,DL模型显著优于传统ML模型.
- 在半模拟数据 (斯皮尔曼相关性=0.94) 和实验数据 (斯皮尔曼相关性=0.96) 上,DL-SHAP表现出强大的性能.
- 确定与MMSE预测和AD严重程度相关的等级主导的大脑区域.
结论:
- 可解释的人工智能方法DL-SHAP有效地从大型MRI数据集预测全球认知.
- 在AD连续中,DL-SHAP成功地确定了多变量全脑认知关系.
- 该方法为预测临床严重性提供了令人信服的证据,并突出了关键的大脑区域.
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