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A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease

Rana, Sijan S.; Ma, Xinhui; Pang, Wei; Wolverson, Emma

Authors

Sijan S. Rana

Wei Pang

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Dr Emma Wolverson E.Wolverson@hull.ac.uk
Reader in Ageing and Dementia. Research Lead for Dementia UK.



Abstract

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utilised in identifying at-risk individuals and directing additional medical treatment in order to revert conversion to AD as well as provide psychosocial support for the person and their family.This paper presents a novel solution in the early detection of pMCI people and classification of AD risk within MCI people. We proposed a model, MudNet, to utilise deep learning in the simultaneous prediction of progressive/stable MCI classes and time-to-AD conversion where high-risk pMCI people see conversion to AD within 24 months and low-risk people greater than 24 months. MudNet is trained and validated using baseline clinical and volumetric MRI data (n = 559 scans) from participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model utilises T1-weighted structural MRIs alongside clinical data which also contains neuropsychological (RAVLT, ADAS-11, ADAS-13, ADASQ4, MMSE) tests as inputs.The averaged results of our model indicate a binary accuracy of 69.8% for conversion predictions and a categorical accuracy of 66.9% for risk classifications.

Citation

Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. In 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) (9-18). https://doi.org/10.1109/BDCAT50828.2020.00013

Conference Name 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
Conference Location Leicester, United Kingdom
Start Date Dec 7, 2020
End Date Dec 10, 2020
Acceptance Date Oct 18, 2020
Online Publication Date Dec 28, 2020
Publication Date 2020-12
Deposit Date Jan 11, 2021
Publicly Available Date Aug 24, 2021
Publisher Institute of Electrical and Electronics Engineers
Pages 9-18
Book Title 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
ISBN 9780738123967
DOI https://doi.org/10.1109/BDCAT50828.2020.00013
Public URL https://hull-repository.worktribe.com/output/3692816
Publisher URL https://ieeexplore.ieee.org/xpl/conhome/9302521/proceeding

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