在帕金森病中预测五年内认知衰退的机器学习:比较认知评估尺度与DAT SPECT和临床生物标志物
Arman Gorji1,2, Ali Fathi Jouzdani1,2
1Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Neuroscience and Artificial Intelligence Research Group (NAIRG), Hamadan University of Medical Sciences, Hamadan, Iran.
PloS one
|July 17, 2024
概括
蒙特利尔认知评估 (MoCA) 规模有效地预测了帕金森病 (PD) 在五年内认知衰退. 来自DAT SPECT扫描的深度放射性特征提高了PD患者的预测准确度.
科学领域:
- 神经科学是一个神经科学.
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 帕金森病 (PD) 主要以运动症状而闻名,但诸如认知障碍之类的非运动症状很普遍.
- 监测PD的认知衰退对于患者的管理和护理至关重要.
- 现有的认知尺度需要对其在长期PD进展中的预测能力进行评估.
研究的目的:
- 确定最有效的认知尺度,在5年内预测PD的认知衰退.
- 评估临床生物标志物和DAT SPECT成像与机器学习一起用于PD认知衰退预测的实用性.
主要方法:
- 利用机器学习算法与DAT SPECT图像和来自330名帕金森病进展标记计划 (PPMI) 参与者的临床数据.
- 开发了一个3D自编码器,从DAT SPECT扫描中提取深度放射性特征.
- 结合深度放射性特征与17个临床特征,使用蒙特利尔认知评估 (MoCA) 和MDS-UPDRS-I尺度预测认知衰退.
主要成果:
- 与多个时间点的MDS-UPDRS-I尺度相比,MoCA尺度在预测认知衰退方面表现优越.
- 来自DAT SPECT的深度放射性特征实现了最高的预测准确性,在4年使用MoCA的梯度增强分类器时,交叉验证AUC为89.28%.
- 对于MDS-UPDRS-I,深度放射性特征产生了随机森林分类器的最大交叉验证AUC为81.34%.
结论:
- 摩卡尺度比MDS-UPDRS-I更有效地预测了PD的5年认知衰退.
- 从DAT SPECT扫描中提取的深度放射性特征比单独或组合的临床生物标志物提供更好的预测性能.
- 将MoCA分数与DAT SPECT衍生的深度放射性特征集成,为早期识别帕金森病认知衰退风险提供了一个有希望的策略.
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