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Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions

Citation

Li, R and Wang, X and Lawler, K and Garg, S and Bai, Q and Alty, J, Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions, Journal of Biomedical Informatics, 127 Article 104030. ISSN 1532-0464 (2022) [Refereed Article]

Copyright Statement

© 2022 Elsevier Inc. All rights reserved.

DOI: doi:10.1016/j.jbi.2022.104030

Abstract

Background & Objective

With populations aging, the number of people with dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases are due to Alzheimer’s disease (AD) pathology and there is a 10–20 year ’pre-clinical’ period before significant cognitive decline occurs. We urgently need, cost effective, objective biomarkers to detect AD, and other dementias, at an early stage. Risk factor modification could prevent 40% of cases and drug trials would have greater chances of success if participants are recruited at an earlier stage. Currently, detection of dementia is largely by pen and paper cognitive tests but these are time consuming and insensitive to the pre-clinical phase. Specialist brain scans and body fluid biomarkers can detect the earliest stages of dementia but are too invasive or expensive for widespread use. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. This scoping review aims to summarise the current capabilities of AI-aided digital biomarkers to aid in early detection of dementia, and also discusses potential future research directions.

Methods & Materials

In this scoping review, we used PubMed and IEEE Xplore to identify relevant papers. The resulting records were further filtered to retrieve articles published within five years and written in English. Duplicates were removed, titles and abstracts were screened and full texts were reviewed.

Results

After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication. A further 771 records were excluded after screening titles and abstracts, and 496 were excluded after full text review. The final yield was 177 studies. Records were grouped into different artificial intelligence based tests: (a) computerized cognitive tests (b) movement tests (c) speech, conversion, and language tests and (d) computer-assisted interpretation of brain scans.

Conclusions

In general, AI techniques enhance the performance of dementia screening tests because more features can be retrieved from a single test, there are less errors due to subjective judgements and AI shifts the automation of dementia screening to a higher level. Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%. In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%. Deep learning techniques applied in brain scan analysis achieves around 92% accuracy. Movement tests and setting smart environments to capture daily life behaviours are two potential future directions that may help discriminate dementia from normal aging. AI-based smart environments and multi-modal tests are promising future directions to improve detection of dementia in the earliest stages.

Item Details

Item Type:Refereed Article
Keywords:artificial intelligence, digital biomarkers, pre-clinical dementia, screening tests, Alzheimer’s
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Li, R (Mr Renjie Li)
UTAS Author:Wang, X (Miss Xinyi Wang)
UTAS Author:Lawler, K (Dr Katherine Lawler)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Bai, Q (Dr Quan Bai)
UTAS Author:Alty, J (Associate Professor Jane Alty)
ID Code:148954
Year Published:2022
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2022-02-24
Last Modified:2022-03-07
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