Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points. (January 2022)

• Record Type: Journal Article
• Authors: Qiao, Hezhe; Chen, Lin; Zhu, Fan
• Is Part Of: Computer methods and programs in biomedicine. Volume 213(2022)
• Publication Date: 2022-01
• Subjects: Alzheimer's Disease (AD) -- Convolutional Neural Network (CNN) -- Magnetic Resonance Imaging (MRI) -- Mini-Mental State Examination (MMSE) -- Ranking learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106503
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We introduce a rankCNN to complete the prediction of MMSE for early AD diagnosis. We transform the MMSE regression into multi-classifications by exploring the relationship between the subject's MMSE and cardinality. We develop a ranking layer to find more subtle features of subjects with different scores. The method can effectively predict the MMSE at baseline and future time points using baseline MRI. Abstract: Background and objective: Alzheimer's disease (AD) is a fatal neurodegenerative disease. Predicting Mini-mental state examination (MMSE) based on magnetic resonance imaging (MRI) plays an important role in monitoring the progress of AD. Existing machine learning based methods cast MMSE prediction as a single metric regression problem simply and ignore the relationship between subjects with various scores. Methods: In this study, we proposed a ranking convolutional neural network (rankCNN) to address the prediction of MMSE through muti-classification. Specifically, we use a 3D convolutional neural network with sharing weights to extract the feature from MRI, followed by multiple sub-networks which transform the cognitive regression into a series of simpler binary classification. In addition, we further use a ranking layer to measure the ranking information between samples to strengthen the ability of the classification by extracting more discriminative features. Results: We evaluated the proposed model on ADNI-1 and ADNI-2 datasets with a total of 1, 569 subjects. The Root Mean Squared Error (RMSE) of our proposed model at baseline is 2.238 and 2.434 on ADNI-1 and ADNI-2, respectively. Extensive experimental results on ADNI-1 and ADNI-2 datasets demonstrate that our proposed model is superior to several state-of-the-art methods at both baseline and future MMSE prediction of subjects. Conclusion: This paper provides a new method that can effectively predict the MMSE at baseline and future time points using baseline MRI, making it possible to use MRI for accurate early diagnosis of AD. The source code is freely available at https://github.com/fengduqianhe/ADrankCNN-master .