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Review
. 2023 Dec;19(12):5952-5969.
doi: 10.1002/alz.13463. Epub 2023 Oct 14.

Artificial intelligence for dementia prevention

Danielle Newby  1 , Vasiliki Orgeta  2 , Charles R Marshall  3   4 , Ilianna Lourida  5   6 , Christopher P Albertyn  7 , Stefano Tamburin  8 , Vanessa Raymont  1 , Michele Veldsman  9   10 , Ivan Koychev  1 , Sarah Bauermeister  1 , David Weisman  11 , Isabelle F Foote  3   12 , Magda Bucholc  13 , Anja K Leist  14 , Eugene Y H Tang  5 , Xin You Tai  15   16 ; Deep Dementia Phenotyping (DEMON) Network; David J Llewellyn  6   17 , Janice M Ranson  6
Affiliations
Review

Artificial intelligence for dementia prevention

Danielle Newby et al. Alzheimers Dement. 2023 Dec.

Abstract

Introduction: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

Methods: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

Results: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

Discussion: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

Highlights: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Keywords: artificial intelligence; dementia; machine learning; prevention; risk prediction.

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Conflict of interest statement

Conflicts of interest

VO is a commissioner of the 2017 and the 2020 Lancet Commission on dementia prevention, intervention, and care. DCW has served on advisory boards with Roche, Biogen and Merck. VR has provided consultancy to Biogen. AKL has served on advisory boards with Roche. DPUK is a DEMON partner. All other authors declare no conflicts of interest.

Figures

Figure 1.
Figure 1.
Growth in citations related to ML/AI in dementia prevention (1990 – 2022). Source: PubMed citations using the search term (Alzheimer*[Title/Abstract] OR dement*[Title/Abstract]) AND (Prevent*[Title/Abstract] OR risk factor*[Title/Abstract] OR determinant*[Title/Abstract]) AND (AI[Title/Abstract] OR artificial intelligence[Title/Abstract] OR machine learning[Title/Abstract])
Figure 2.
Figure 2.. Modifiable risk factors for dementia identified in prior systematic reviews and guidelines.
Visualisation of risk factors identified in (a) five prior systematic reviews and, (b) the recent WHO guidelines (2019) and Lancet Commission (2020). Risk factors (circles) were divided into health risk, lifestyle and environmental categories. The number of reviews which identified each risk factor is reflected by the relative size of each circle (shaded indicates comparatively stronger evidence, * denotes a protective factor while ‘E’ and ‘M’ represent early and mid-life risk factors, respectively. Anticoag: anticoagulant, BDZ: benzodiazepines, post- op delirium: post-operative delirium, Cerebral SVD: Cerebral small vessel disease
See this image and copyright information in PMC

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