基于双重注意力卷积网络,预测从正常认知到轻度认知障碍的转换时间
Xiawei Zhu1, Sibo Liu2, Long Wang1
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Haidian District, Beijing, China.
Journal of Alzheimer's disease : JAD
|October 17, 2025
概括
一个新的双重注意力卷积网络准确地预测了从正常认知到轻度认知障碍的转换时间. 这一进步有助于早期诊断和治疗痴呆症.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 痴呆症是一种进展性神经系统疾病,是全球主要的死亡原因.
- 早期诊断,预防和治疗对于管理痴呆的影响至关重要.
- 痴呆症在最初的阶段往往被诊断不足.
研究的目的:
- 准确预测从正常认知 (NC) 到轻度认知障碍 (MCI) 的转换时间.
- 为早期诊断和治疗痴呆提供见解.
- 开发一种用于预测神经退行性疾病进展的模型.
主要方法:
- 开发了一种新的双重注意力卷积网络.
- 该模型整合了特征和时间注意模块,用于捕获依赖性.
- 使用定制损失函数来提高临床解释性.
主要成果:
- 该模型显著提高了预测准确性,将MSE降低了9.67%和MAE降低了26.24%.
- 该模型显示,与基本卷积模型相比,R平方增加了16.71%.
- 该模型成功预测了NC转换为MCI,指导了早期干预策略.
结论:
- 双重注意力卷积网络是预测NC转换为MCI的有效工具.
- 这个模型为早期痴呆症诊断提供了宝贵的支持.
- 这些发现有助于推进神经退行性疾病的管理.
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