在65岁及以上的老年人中,根据深度学习方法,预测从正常认知功能发展为阿尔茨海默病的风险
Yue Liu1,2, Haixia Su2,3, Tianyuan Guan1,2
1Department of Health Statistics, School of Public Health, Fourth Military Medical University, Xi'an, Shaanxi, China.
Journal of Alzheimer's disease : JAD
|January 6, 2026
概括
深度学习模型在预测认知正常的老年人中预测阿尔茨海默病 (AD) 风险方面表现有前途. 这些模型为早期AD风险评估提供了稳定而准确的方法,有助于及时干预.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经系统疾病,没有治愈方法,需要早期预测风险才能有效管理.
- 在认知正常的老年人中预测AD风险对于及时预防,治疗启动和护理规划至关重要.
研究的目的:
- 开发和评估深度学习 (DL) 模型,用于预测老年人从正常认知到阿尔茨海默病 (AD) 的进展.
- 为临床决策和早期AD查工具的开发提供可靠的参考.
主要方法:
- 构建DeepSurv,DeepHit和Cox回归模型用于风险预测.
- 使用一致性指数 (C指数),综合布莱尔得分 (IBS) 和ROC曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 深度学习模型表现出稳定的预测性能,DeepHit的C指数为0.83,IBS为0.07.
- 与基于C指数值的传统Cox模型相比,深度学习模型表现出更高的一致性.
- 所有模型都显示出相对稳定的整体预测能力,表明临床应用的潜力.
结论:
- 对阿尔茨海默病进展的风险预测模型可以使用易于获得的早期预测因素有效建立.
- 深度学习方法为开发准确可靠的查工具提供了强大的框架,用于早期对老年人的AD风险评估.
- 建议进一步进行临床验证,以将这些模型纳入常规医疗保健实践,以快速查AD风险.
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