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用动脉脊柱标记MRI基于机器学习的不同运动亚型的帕金森病的自动分类

Jinhua Xiong¹, Haiyan Zhu², Xuhang Li³

¹Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong New Area, Shanghai 200120, China.

|November 25, 2023

概括

支持矢量机 (SVM) 通过动脉旋转标记MRI成功分类帕金森病 (PD) 亚型. 特定的大脑区域显示出不同的血液 perfusion 模式,为个性化治疗提供潜在的成像标记.

科学领域:

神经成像是一种神经成像.
机器学习机器学习
神经学神经学

背景情况:

帕金森病 (PD) 呈现出多种运动亚型,包括震主导 (TD) 和姿势不稳定 (PIGD).
这些亚型的准确分类对于有针对性的治疗策略至关重要.

研究的目的:

开发一种自动化方法,使用动脉旋转标记磁共振成像 (ASL-MRI) 数据来分类PD电机亚型.
识别特定的大脑区域及其相关的血液 perfusion 模式,表明不同的PD亚型.

主要方法:

利用支持矢量机器 (SVM) 来对38名受试者 (21名PD患者,17名正常对照) 的ASL-MRI数据进行二进制分类.
使用自动化解剖图谱3 (AAL3) 模板从170个大脑区域提取voxel值.
分类正常对照 (NCs) 与PD患者,并区分TD和PIGD亚型.

主要成果:

在使用左下前带皮层 (ACC_sub_L) 来区分PD患者和NC患者 (92.31%) 中,SVM实现了高诊断准确度.
在SVM的研究中,SupraMarginal_R使用右侧超边缘环 (SupraMarginal_R) 以84.21%的准确度将TD亚型与其他组区分开来.
在SVM中,PIGD亚型的确诊准确率为89.47%,使用 thalamus 的右侧内膜 (Thal_IL_R),在大脑皮质皮层和条状皮质皮层循环中显示出不同的血液 perfusion 模式.

结论:

与SVM相结合的ASL-MRI可以有效地分类PD电机亚型.
确定了特定的大脑区域 (ACC_sub_L,SupraMarginal_R,Thal_IL_R) 作为PD及其亚型的潜在成像生物标志物.
这些发现支持了PD亚型在关键神经回路 (CTC和STC) 中差异性血液 perfusion 的假设,为个性化治疗方法铺平了道路.

关键词:

帕金森病是帕金森氏症的一种疾病.动脉旋转标签的标签机器学习是机器学习.发动机子类型发动机子类型支持矢量机器的支持矢量机器.

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