生物标志物 生物标志物
Sukhman Singh1, Sofia Michopoulou2, Xiaoxiao Li3
1Faculty of Medicine, University of Southampton, Southampton, United Kingdom.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
人工智能 (AI) 在使用粉样蛋白PET扫描诊断阿尔茨海默病 (AD) 方面表现有前途. 虽然模型取得了中等准确性,但需要进一步研究更大的数据集,以提高AD的诊断性能.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 诊断正在转向生理测试,包括神经成像,如正子发射断层扫描 (PET) 和液体生物标志物.
- 目前的诊断过程是漫长的,平均为两年,突出了需要更高效的方法.
- 人工智能 (AI) 提供了分析PET扫描的潜力,以识别患有AD的患者.
研究的目的:
- 通过使用粉样PET成像来研究可解释AI在诊断阿尔茨海默病方面的潜力.
- 评估AI模型在根据特定大脑区域的粉样蛋白负载对患者进行分类时的性能.
主要方法:
- 利用了阿尔茨海默病神经成像倡议 (ADNI) 研究中541名患者的医疗数据和PET扫描.
- 训练有素的人工智能模型对来自有兴趣的大脑区域的粉样蛋白负载数据进行训练,以将患者分类为正常或受损.
- 使用粉样β,和化生物标志物值测试人工智能模型,并将机器学习算法应用于多达10个大脑区域.
主要成果:
- 酸化 (p-tau) 模型实现了最高的测试准确率,达到72.8%.
- 粉样β (Aβ) 生物标志物模型表现出优异的整体分类性能,曲线下的面积 (AUC) 为0.80.
- 沙普利的重要性值确定了前骨和左舌环为Aβ模型的重要区域.
结论:
- 可解释的AI显示了从粉样蛋白PET扫描中对AD诊断的潜力,在相关的大脑区域实现了中度的准确性.
- 更大的数据集和诸如分层交叉验证之类的技术是必要的,以提高准确性和解决数据集不平衡.
- 人工智能驱动的神经成像生物标志物的分析有望提高阿尔茨海默病诊断的效率和准确性.
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