  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.
