Prediction of conversion to Alzheimer's disease using deep survival analysis of MRI images.

ABSTRACT: The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer's disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer's disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distrib

SUBMITTER: Nakagawa T 

PROVIDER: S-EPMC7425528 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature