A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI

by   Hongming Li, et al.

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 MRI 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 (C-index) of 0.762 on 439 ADNI testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a C-index of 0.781 on 40 AIBL testing MCI subjects with follow-up duration from 18-54 months (quartiles: [18, 36,54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (p<0.0002). Improved performance for predicting progression to AD dementia (C-index=0.864) was obtained when the deep learning based progression risk was combined with baseline clinical measures. Conclusion: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.


Representing Alzheimer's Disease Progression via Deep Prototype Tree

For decades, a variety of predictive approaches have been proposed and e...

Reinforcement Learning based Disease Progression Model for Alzheimer's Disease

We model Alzheimer's disease (AD) progression by combining differential ...

Cortical Morphometry Analysis based on Worst Transportation Theory

Biomarkers play an important role in early detection and intervention in...

DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease

Volume change measures derived from longitudinal MRI (e.g. hippocampal a...

Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline

We propose a method to predict the subject-specific longitudinal progres...

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

Background: Alzheimer's Disease (AD) is the most common type of age-rela...

Please sign up or login with your details

Forgot password? Click here to reset