基于脑代谢成像的模型确定了在前发性阿尔茨海默氏症患者的认知稳定性
Jarrad Perron1,2, Carly Scramstad3, Ji Hyun Ko4,5,6
1Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, 75 Chancellor's Circle, Winnipeg, MB, R3T 5V6, Canada.
Scientific reports
|May 17, 2025
概括
一个新的AI模型准确地识别了可能进展的早期阿尔茨海默病 (AD) 患者,有助于选择抗粉样蛋白疗法. 这种工具有助于预测轻度认知障碍 (MCI) 患者的认知衰退,优化治疗策略.
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:
- 最近批准用于阿尔茨海默病 (AD) 的抗粉样类药物需要精确的患者选择.
- 患有AD生物标志物的轻度认知障碍 (MCI) 患者可能不会进展,从而使治疗决策复杂化.
研究的目的:
- 开发和验证一种预测模型,用于预兆性AD的认知衰退.
- 为了确定那些将发展为痴呆症的患者和那些将保持稳定的患者.
主要方法:
- 通过使用来自594名ADNI患者的氧糖PET和生物标志物数据,训练了一个神经网络组合.
- 该模型从基线MCI诊断数据预测认知,重点关注生物AD患者.
主要成果:
- 整体在曲线下的面积达到88.6%,可以区分渐进型和稳定的MCI患者.
- 在对渐进性MCI患者进行分类时,观察到高精度 (88.6%) 和特异性 (100%).
- 该模型还在分类健康个体和AD连续体中的个体方面表现出强的表现.
结论:
- 开发的AI模型可以有效地识别那些不太可能发展为痴呆症的前性AD患者.
- 这种工具有可能降低社会成本,并优化对抗粉样蛋白疗法和临床试验候选人的选择.
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