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

TL;DR

This study develops a deep learning model that predicts the progression of mild cognitive impairment to Alzheimer's disease using hippocampal MRI scans, achieving high accuracy and aiding clinical prognosis.

Contribution

It introduces a novel deep learning time-to-event model trained on large MRI datasets to predict individual progression to Alzheimer's disease.

Findings

C-index of 0.762 on ADNI data

C-index of 0.781 on AIBL data

Enhanced prediction accuracy when combined with clinical measures

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 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.