基于深度学习的粉样蛋白PET协调,预测非痴呆老年人的认知衰退.
Yoon Seong Choi1,2, Pei Ing Ngam3, Jeong Ryong Lee4
1Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore.
Radiology advances
|October 8, 2025
概括
粉样PET的深度学习协调改善了对非痴呆症患者认知衰退的预测. 这种新的方法提高了预后准确性,这表明它可以补充现有的粉样蛋白PET措施,用于早期发现阿尔茨海默病.
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:
- 传统的粉样PET对标记剂的协调缺乏稳定性.
- 准确预测认知衰退对于早期阿尔茨海默病 (AD) 干预至关重要.
研究的目的:
- 为了评估基于深度学习的粉样蛋白PET的协调,预测从认知不受损 (CU) 转换为轻度认知障碍 (MCI) 和MCI转换为AD.
- 评估深度学习衍生的粉样蛋白PET概率评分 (DL-ADprob) 的预后性能.
主要方法:
- 开发了一种深度学习模型,通过使用ADNI,日本ADNI和AIBL队列 (n=1050) 的数据在多个粉样蛋白PET标记器中对AD-痴呆症和CU进行分类.
- 评估DL-ADprob在预测ADNI-MCI (n=451) 和HABS-CU (n=271) 参与者的认知衰退中的预后价值超过4年.
- 在GAIN数据集 (n=155) 中的标记物中计算DL-ADprob的类内相关系数 (ICC).
主要成果:
- 在ADNI-MCI (P<.001) 和HABS-CU (P=.048) 队列中,DL-ADprob的预后是独立的.
- 在ADNI-MCI (tdAUC 0.758至0.782) 和HABS-CU (tdAUC 0.846至0.870) 组中,DL-ADprob的加入改善了预后性能.
- DL-ADprob在所有标记物中显示出很高的一致性 (ICCs 0.913-0.935).
结论:
- 基于深度学习的粉样PET的协调显著提高了对非痴呆症患者认知能力下降的预测.
- DL-ADprob显示出作为常规粉样蛋白PET测量工具的补充工具的潜力,以提高早期AD检测的预后准确性.
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