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

Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 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 < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures. Discussion: 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.

Original languageEnglish (US)
Pages (from-to)1059-1070
Number of pages12
JournalAlzheimer's and Dementia
Volume15
Issue number8
DOIs
StatePublished - Aug 2019

Keywords

  • Alzheimer's disease
  • Deep learning
  • Hippocampus
  • Time-to-event analysis

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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