Keywords: Dementia, Dementia, Machine Learning
Motivation: Alzheimer’s disease (AD) and normal pressure hydrocephalus (NPH) are dementia-causing neurologic disorders. NPH is highly treatable whereas AD is not. Clinically, it is important to differentiate the two disorders.
Goal(s): To improve the MR Elastography-based classification of AD and NPH cases.
Approach: Apply regional analysis of MR Elastography-derived mechanical properties (shear stiffness and compressibility metric) of the brain as features in a machine learning classifier to distinguish between AD, NPH, and healthy participants.
Results: The derived compressibility metric is a complementary mechanical property to the shear stiffness for improving the classification of AD and NPH cases.
Impact: We used a lobar atlas based regional values of MR Elastography derived mechanical properties to improve machine learning classification of cognitively unimpaired controls, and normal pressure hydrocephalus and Alzheimer’s dementia patients.
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