生物标志物 生物标志物
Gauri Darekar1, Taslim Murad1, Hui-Yuan Miao1
1Washington University in St. Louis, St. Louis, MO, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
这项研究使用人工智能 (AI) 来从MRI扫描中预测大脑年龄,确定与正常衰老和阿尔茨海默病 (AD) 相关的关键大脑区域. 人工智能模型准确地预测了大脑年龄,并揭示了与AD严重程度相关的模式.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 年龄是轻度认知障碍和阿尔茨海默病 (MCI/AD) 的主要危险因素.
- 了解正常的大脑衰老和MCI/AD需要识别大脑年龄模式.
- 以前的研究集中在个别的大脑区域上,忽视了多变量关联.
研究的目的:
- 开发和验证人工智能模型,使用MRI数据预测大脑年龄.
- 为了确定与大脑年龄预测相关的显著多变量大脑区域.
- 探索大脑年龄模式与MCI/AD临床严重程度之间的关系.
主要方法:
- 利用深度学习 (AgeNet) 和机器学习模型从区域大脑体积来估计大脑年龄.
- 将最佳AI模型与SHAP集成,用于识别关键的多变量大脑区域.
- 在模拟和实验MRI数据集 (n=668) 上验证了方法.
主要成果:
- 深度学习模型 (AgeNet) 在大脑年龄预测方面显著优于传统模型.
- 在模拟中,AgeNet-SHAP成功地确定了大脑年龄的关键预测因素.
- 与对照组相比,在阿尔茨海默氏症患者中观察到大脑结构的显著,广泛的区域差异.
结论:
- 可解释的人工智能 (AgeNet-SHAP) 能够有效地预测大脑年龄,并揭示多变量大脑区域的关联.
- 该方法确定了与阿尔茨海默病严重程度相关的大脑区域.
- 这些发现支持人工智能驱动的诊断,预后和神经退行性疾病个性化医学方法.
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