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Deep recurrent model for individualized prediction of Alzheimer's disease progression

Authors
Jung, WonsikJun, EunjiSuk, Heung-Il
Issue Date
15-8월-2021
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Keywords
Alzheimer' s Disease; Cognitive tests; Conversion-Time Prediction; Deep Learning; Disease Progression Modeling; Longitudinal Data; Mild Cognitive Impairment; Missing Value Imputation; Recurrent Neural Networks
Citation
NEUROIMAGE, v.237
Indexed
SCIE
SCOPUS
Journal Title
NEUROIMAGE
Volume
237
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/136847
DOI
10.1016/j.neuroimage.2021.118143
ISSN
1053-8119
Abstract
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling. Under the same problem settings, in this work, we propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status along with cognitive scores at multiple future time points. However, in handling time series data, it generally faces many unexpected missing observations. In regard to such an unfavorable situation, we define a secondary problem of estimating those missing values and tackle it in a systematic way by taking account of temporal and multivariate relations inherent in time series data. Concretely, we propose a deep recurrent network that jointly tackles the four problems of (i) missing value imputation, (ii) phenotypic measurements forecasting, (iii) trajectory estimation of a cognitive score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers. Notably, the learnable parameters of all the modules in our predictive models are trained in an end-to-end manner by taking the morphological features and cognitive scores as input, with our circumspectly defined loss function. In our experiments over The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort, we measured performance for various metrics and compared our method to competing methods in the literature. Exhaustive analyses and ablation studies were also conducted to better confirm the effectiveness of our method.
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