artificial intelligence; dementia; machine learning; prevention; risk prediction; Epidemiology; Health Policy; Developmental Neuroscience; Neurology (clinical); Geriatrics and Gerontology; Cellular and Molecular Neuroscience; Psychiatry and Mental Health
Abstract :
[en] 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.
Research center :
Integrative Research Unit: Social and Individual Development (INSIDE) > PEARL Institute for Research on Socio-Economic Inequality (IRSEI)
Precision for document type :
Review article
Disciplines :
Public health, health care sciences & services Sociology & social sciences Social & behavioral sciences, psychology: Multidisciplinary, general & others Computer science
Author, co-author :
Newby, Danielle ; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
Orgeta, Vasiliki; Division of Psychiatry, University College London, London, UK
Marshall, Charles R; Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK ; Department of Neurology, Royal London Hospital, London, UK
Lourida, Ilianna; Population Health Sciences Institute, Newcastle University, Newcastle, UK ; University of Exeter Medical School, Exeter, UK
Albertyn, Christopher P; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Tamburin, Stefano; Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
Raymont, Vanessa; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
Veldsman, Michele; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK ; Department of Experimental Psychology, University of Oxford, Oxford, UK
Koychev, Ivan; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
Bauermeister, Sarah; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
Weisman, David; Abington Neurological Associates, Abington, Pennsylvania, USA
Foote, Isabelle F; Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK ; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
Bucholc, Magda; Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
LEIST, Anja ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) > Socio-Economic Inequality
Tang, Eugene Y H; Population Health Sciences Institute, Newcastle University, Newcastle, UK
Tai, Xin You; Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK ; Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, UK
Llewellyn, David J ✱; University of Exeter Medical School, Exeter, UK ; The Alan Turing Institute, London, UK
Ranson, Janice M ✱; University of Exeter Medical School, Exeter, UK
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Artificial intelligence for dementia prevention
Publication date :
December 2023
Journal title :
Alzheimer's and Dementia: the Journal of the Alzheimer's Association
ISSN :
1552-5260
eISSN :
1552-5279
Publisher :
John Wiley and Sons Inc, United States
Volume :
19
Issue :
12
Pages :
5952-5969
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Development Goals :
3. Good health and well-being
European Projects :
H2020 - 803239 - CRISP - Cognitive Aging: From Educational Opportunities to Individual Risk Profiles
Funders :
Medical Research Council European Research Council Barts Charity Union Européenne
Funding text :
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.
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