Diffusion Tensor Imaging Biomarkers for Parkinson's Disease Symptomatology

被引：0

作者：

Romero, Alfonso Estudillo ^{[1
]}

Haegelen, Claire ^{[1
,2
]}

Jannin, Pierre ^{[1
]}

Baxter, John S. H. ^{[1
]}

机构：

[1] INSERM, Lab Traitement Signal & lImage, UMR 1099, LTSI, Univ Rennes 1, Rennes, France

[2] Hospices Civiles Lyon, Dept NeuroSurg, Lyon, France

来源：

ARTIFICIAL INTELLIGENCE OVER INFRARED IMAGES FOR MEDICAL APPLICATIONS AND MEDICAL IMAGE ASSISTED BIOMARKER DISCOVERY | 2022年 / 13602卷

关键词：

Parkinson's disease; Diffusion tensor imaging; Convolutional neural networks;

D O I：

10.1007/978-3-031-19660-7_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Voxel-based analysis is an invaluable tool for biomarker discovery in population neuroimaging. The traditional approach however is limited to local, linear biomarkers, determining if the linear correlation between the quantitative value of an image is correlated with the disease state at a single voxel. By analysing convolutional neural networks that directly predict clinical scores using a newly proposed voxel-based diktiometry, non-linear and non-local biomarkers can be visualised, leading to an additional tool for biomarker discovery. Our approach using diffusion tensor images to predict UPDRS3 and Hoehn & Yahr scores for Parkinson's disease patients, shows consistent and explainable results.

引用

页码：134 / 142

页数：9

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