利用生物流体、神经影像学和人工智能识别帕金森病中的认知障碍。
The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.
作者信息
Dennis Anthaea-Grace Patricia, Strafella Antonio P
机构信息
Krembil Brain Institute, University Health Network (UHN), Toronto, ON, Canada.
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
出版信息
Front Neurosci. 2024 Sep 26;18:1446878. doi: 10.3389/fnins.2024.1446878. eCollection 2024.
INTRODUCTION
Parkinson's disease (PD) is a neurodegenerative movement disorder causing severe disability and cognitive impairment as the disease progresses. It is necessary to develop biomarkers for cognitive decline in PD for earlier detection and prediction of disease progression.
METHODS
We reviewed literature which used artificial intelligence-based techniques, which can be more sensitive than other analyses, to determine potential biomarkers for cognitive impairment in PD.
RESULTS
We found that combining biomarker types, including those from neuroimaging and biofluids, resulted in higher accuracy. Focused analysis on each biomarker type revealed that using structural and functional magnetic resonance imaging (MRI) resulted in accuracy and area under the curve (AUC) values above 80%/0.80, and that beta-amyloid-42 and tau were able to classify PD subjects by cognitive function with accuracy and AUC values above 90%/0.90.
DISCUSSION
We can conclude that applying both blood-based and imaging-based biomarkers may improve diagnostic accuracy and prediction of cognitive impairment in PD.
引言
帕金森病（PD）是一种神经退行性运动障碍，随着疾病进展会导致严重残疾和认知障碍。有必要开发用于帕金森病认知衰退的生物标志物，以便更早地检测和预测疾病进展。
方法
我们回顾了使用基于人工智能技术的文献，这些技术可能比其他分析方法更敏感，用于确定帕金森病认知障碍的潜在生物标志物。
结果
我们发现，结合生物标志物类型，包括来自神经影像学和生物流体的类型，可提高准确性。对每种生物标志物类型的重点分析表明，使用结构和功能磁共振成像（MRI）时，准确率和曲线下面积（AUC）值高于80%/0.80，并且β-淀粉样蛋白42和tau能够根据认知功能对帕金森病患者进行分类，准确率和AUC值高于90%/0.90。
讨论
我们可以得出结论，应用基于血液和基于成像的生物标志物可能会提高帕金森病认知障碍的诊断准确性和预测能力。
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