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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publication2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
PublisherIEEE
ISBN (Electronic)9780738123967
DOIs
Publication statusAccepted/In press - 23 Oct 2020
Event7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies 2020 - online, Leicester , United Kingdom
Duration: 7 Dec 202010 Dec 2020

Other

Other7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies 2020
Abbreviated titleBDCAT 2020
Country/TerritoryUnited Kingdom
CityLeicester
Period7/12/2010/12/20

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