Liu, Y., Mazumdar, S. orcid.org/0000-0002-0748-7638 and Bath, P.A. orcid.org/0000-0002-6310-7396 (2023) An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans. International Journal of Medical Informatics, 173. 105027. ISSN 1386-5056
Abstract
Background: Alzheimer's disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. Due to the lack of effectiveness of manual diagnosis by doctors, machine learning is now being applied to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. Objective: This study applied a statistical method and unsupervised learning methods to discriminate between scans from cognitively normal (CN) and people with AD using a limited number of labelled structural MRI scans. Methods: We used two-sample t-tests to detect the AD-relevant regions, and then employed an unsupervised learning neural network to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on baseline brain structural MRI scans from 429 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of which 231 were CN and 198 had AD. Results: The abnormal regions around the lower parts of limbic system were indicated as AD-relevant regions based on the two-sample t-test (p < 0.001), and the proposed method yielded an accuracy of 0.84 for discriminating between CN and AD. Conclusion: The study combined statistical and unsupervised learning methods to identify scans of people with AD. This method can detect AD-relevant regions and could be used to accurately diagnose AD; it does not require large amounts of labelled MRI scans. Our research could help in the automatic diagnosis of AD and provide a basis for diagnosing stable mild cognitive impairment (stable MCI) and progressive mild cognitive impairment (progressive MCI).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Elsevier. This is an author produced version of a paper subsequently published in International Journal of Medical Informatics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Alzheimer’s disease; Deep learning; MRI; Machine learning; Unsupervised learning; Humans; Alzheimer Disease; Unsupervised Machine Learning; Magnetic Resonance Imaging; Brain; Neuroimaging |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Nov 2023 09:18 |
Last Modified: | 02 Mar 2024 01:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.ijmedinf.2023.105027 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205451 |
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