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

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, December). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Presented at 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
• 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