从聚合的临床试验中预测阿尔茨海默病的快速进展的深度学习:一项回顾性研究
Xiaotian Ma1, Madison Shyer2, Kristofer Harris2
1Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
PLOS digital health
|April 10, 2024
概括
在阿尔茨海默病 (AD) 临床试验中识别快速进展者 (RP) 是至关重要的. 深度学习模型使用神经认知和生物标记数据准确预测RP,帮助药物开发.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 临床试验 临床试验
背景情况:
- 阿尔茨海默病 (AD) 的进展在患者之间有很大差异.
- 准确识别快速进展者 (RP) 对于临床试验有效性至关重要.
- 目前用于识别RP的方法可能不准确,可能会扭曲治疗结果评估.
研究的目的:
- 开发和验证深度学习模型,用于预测阿尔茨海默病临床试验中的RP.
- 为了确定RP识别的关键预测特征和时间轨迹.
- 通过改进RP预测,提高AD临床试验的设计和效率.
主要方法:
- 利用了来自三项随机对照试验 (远征,远征2,远征3) 的非识别的安慰剂组数据,涉及1603名轻度至中度AD患者.
- 使用具有注意力机制的循环神经网络,根据纵向数据 (神经认知健康,大脑体积,粉样β水平) 预测80周的RP.
- 针对28周观察训练的模型进行了集中评估,通过接收器操作特征曲线 (AUROC) 下面的区域和精度召回曲线 (AUPRC) 下面的区域评估性能.
主要成果:
- 模型表现出强大的内部验证,AUROCs从0.80到0.82,AUPRCs从0.34到0.46.
- 外部验证得出了类似的结果,AUROCs从0.75到0.83和AUPRCs从0.27到0.45.
- 关键预测因素包括粉样β (Aβ) 血水平,区域大脑体积和神经认知评估;RP和非RP之间的时间轨迹不同.
结论:
- 深度学习模型有效地利用早期观察数据预测阿尔茨海默病临床试验中的快速进展.
- 神经认知指标,大脑体积和Aβ水平是快速疾病进展的重要预测指标.
- 这些预测模型为优化临床试验设计和加速开发有效的AD疗法提供了宝贵的工具.
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