A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

ABSTRACT:

Introduction

It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

Methods

A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.

Results

The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian I

SUBMITTER: Li H 

PROVIDER: S-EPMC6719787 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature