Sijan S. Rana
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
Dr Xinhui Ma Xinhui.Ma@hull.ac.uk
Lecturer
Wei Pang
Dr Emma Wolverson E.Wolverson@hull.ac.uk
Senior Lecturer 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 | Oct 27, 2022 |
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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Copyright Statement
Copyright © 2020, IEEE
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