Artificial intelligence for dementia prevention

Danielle Newby; Vasiliki Orgeta; Charles R. Marshall; Ilianna Lourida; Christopher P. Albertyn; Stefano Tamburin; Vanessa Raymont; Michele Veldsman; Ivan Koychev; Sarah Bauermeister; David Weisman; Isabelle F. Foote; Magda Bucholc; Anja K. Leist; Eugene Y. H. Tang; Xin You Tai; David J. Llewellyn; Janice M. Ranson

doi:10.1002/alz.13463

Artificial intelligence for dementia prevention

Danielle Newby, Vasiliki Orgeta, Charles R. Marshall, Ilianna Lourida, Christopher P. Albertyn, Stefano Tamburin, Vanessa Raymont, Michele Veldsman, Ivan Koychev, Sarah Bauermeister, David Weisman, Isabelle F. Foote, Magda Bucholc, Anja K. Leist, Eugene Y. H. Tang, Xin You Tai, , David J. Llewellyn, Janice M. Ranson

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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.

Original language	English
Pages (from-to)	5952-5969
Number of pages	18
Journal	Alzheimer's and Dementia
Volume	19
Issue number	12
Early online date	14 Oct 2023
DOIs	https://doi.org/10.1002/alz.13463
Publication status	Published online - 14 Oct 2023

Bibliographical note

Funding Information:
With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, and Zhi Yao. This review was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. This article was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. J.M.R. and D.J.L. are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). D.J.L. also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). This work was additionally supported by the following: European Research Council (grant agreement no. 803239 (A.K.L.), Barts Charity (C.R.M.), George Henry Woolfe Legacy Fund and the National Institute on Aging (RF1AG073593) (I.F.F.), E.Y.H.T. (National Institute for Health Research (NIHR) Clinical Lecturer) is funded by the NIHR and the views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS, or the UK Department of Health and Social Care. S.B. is supported by Dementias Platform UK (DPUK). The Medical Research Council supports DPUK through grant MR/T0333771. M.B. is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (Special EU Programmes body (SEUPB)) INTERREG (European Region Development Fund (ERDF)/SEUPB), Health and Social Care Research and Development HSC R&D (COM/5750/23) and Dr George Moore Endowment for Data Science at Ulster University.

Publisher Copyright:
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

Keywords

machine learning
artificial intelligence
prevention
risk prediction
dementia

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Newby, D., Orgeta, V., Marshall, C. R., Lourida, I., Albertyn, C. P., Tamburin, S., Raymont, V., Veldsman, M., Koychev, I., Bauermeister, S., Weisman, D., Foote, I. F., Bucholc, M., Leist, A. K., Tang, E. Y. H., Tai, X. Y., & Llewellyn, D. J., & Ranson, J. M. (2023). Artificial intelligence for dementia prevention. Alzheimer's and Dementia, 19(12), 5952-5969. Advance online publication. https://doi.org/10.1002/alz.13463

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title = "Artificial intelligence for dementia prevention",

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 = "machine learning, artificial intelligence, prevention, risk prediction, dementia",

author = "Danielle Newby and Vasiliki Orgeta and Marshall, {Charles R.} and Ilianna Lourida and Albertyn, {Christopher P.} and Stefano Tamburin and Vanessa Raymont and Michele Veldsman and Ivan Koychev and Sarah Bauermeister and David Weisman and Foote, {Isabelle F.} and Magda Bucholc and Leist, {Anja K.} and Tang, {Eugene Y. H.} and Tai, {Xin You} and Llewellyn, {David J.} and Ranson, {Janice M.}",

note = "Funding Information: With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, and Zhi Yao. This review was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. This article was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. J.M.R. and D.J.L. are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). D.J.L. also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). This work was additionally supported by the following: European Research Council (grant agreement no. 803239 (A.K.L.), Barts Charity (C.R.M.), George Henry Woolfe Legacy Fund and the National Institute on Aging (RF1AG073593) (I.F.F.), E.Y.H.T. (National Institute for Health Research (NIHR) Clinical Lecturer) is funded by the NIHR and the views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS, or the UK Department of Health and Social Care. S.B. is supported by Dementias Platform UK (DPUK). The Medical Research Council supports DPUK through grant MR/T0333771. M.B. is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (Special EU Programmes body (SEUPB)) INTERREG (European Region Development Fund (ERDF)/SEUPB), Health and Social Care Research and Development HSC R&D (COM/5750/23) and Dr George Moore Endowment for Data Science at Ulster University. Publisher Copyright: {\textcopyright} 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.",

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Newby, D, Orgeta, V, Marshall, CR, Lourida, I, Albertyn, CP, Tamburin, S, Raymont, V, Veldsman, M, Koychev, I, Bauermeister, S, Weisman, D, Foote, IF, Bucholc, M, Leist, AK, Tang, EYH, Tai, XY & Llewellyn, DJ & Ranson, JM 2023, 'Artificial intelligence for dementia prevention', Alzheimer's and Dementia, vol. 19, no. 12, pp. 5952-5969. https://doi.org/10.1002/alz.13463

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T1 - Artificial intelligence for dementia prevention

AU - Newby, Danielle

AU - Orgeta, Vasiliki

AU - Marshall, Charles R.

AU - Lourida, Ilianna

AU - Albertyn, Christopher P.

AU - Tamburin, Stefano

AU - Raymont, Vanessa

AU - Veldsman, Michele

AU - Koychev, Ivan

AU - Bauermeister, Sarah

AU - Weisman, David

AU - Foote, Isabelle F.

AU - Bucholc, Magda

AU - Leist, Anja K.

AU - Tang, Eugene Y. H.

AU - Tai, Xin You

AU - Llewellyn, David J.

AU - Ranson, Janice M.

N1 - Funding Information: With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, and Zhi Yao. This review was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. This article was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. J.M.R. and D.J.L. are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). D.J.L. also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). This work was additionally supported by the following: European Research Council (grant agreement no. 803239 (A.K.L.), Barts Charity (C.R.M.), George Henry Woolfe Legacy Fund and the National Institute on Aging (RF1AG073593) (I.F.F.), E.Y.H.T. (National Institute for Health Research (NIHR) Clinical Lecturer) is funded by the NIHR and the views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS, or the UK Department of Health and Social Care. S.B. is supported by Dementias Platform UK (DPUK). The Medical Research Council supports DPUK through grant MR/T0333771. M.B. is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (Special EU Programmes body (SEUPB)) INTERREG (European Region Development Fund (ERDF)/SEUPB), Health and Social Care Research and Development HSC R&D (COM/5750/23) and Dr George Moore Endowment for Data Science at Ulster University. Publisher Copyright: © 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

PY - 2023/10/14

Y1 - 2023/10/14

N2 - 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.

AB - 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.

KW - machine learning

KW - artificial intelligence

KW - prevention

KW - risk prediction

KW - dementia

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DO - 10.1002/alz.13463

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SN - 1552-5260

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SP - 5952

EP - 5969

JO - Alzheimer's and Dementia

JF - Alzheimer's and Dementia

IS - 12

ER -

Artificial intelligence for dementia prevention

Abstract

Bibliographical note

Keywords

UN SDGs

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