Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Laura M. Winchester*, Eric L. Harshfield, Liu Shi, Amanpreet Badhwar, Ahmad Al Khleifat, Natasha Clarke, Amir Dehsarvi, Imre Lengyel, Ilianna Lourida, Christopher R. Madan, Sarah J. Marzi, Petroula Proitsi, Anto P. Rajkumar, Timothy Rittman, Edina Silajdžić, Stefano Tamburin, Janice M. Ranson, David J. Llewellyn

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

9 Citations (Scopus)

Abstract

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. Highlights: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Original languageEnglish
Pages (from-to)5860-5871
Number of pages12
JournalAlzheimer's and Dementia
Volume19
Issue number12
Early online date31 Aug 2023
DOIs
Publication statusPublished - Dec 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 paper 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. L.W. is supported by an ARUK Junior Fellowship. E.L.H. is supported by the Cambridge British Heart Foundation Centre of Research Excellence (RE/18/1/34212). A.B. is supported by Fonds de recherche du Québec Santé—Chercheur boursiers Junior 1 and the Fonds de soutien à la recherche pour les neurosciences du vieillissement from the Fondation Courtois. A.A.K. is funded by ALS Association Milton Safenowitz Research Fellowship, The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975‐799), and The NIHR Maudsley Biomedical Research Centre. I.L. receives unrestricted research funding from OPTOS Plc and Hoffman La‐Roche and a grant from Medical Research Council (MR/N029941/1) and Alzheimer's Society UK (Grant No: 6245). 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). P.P. is supported by an ARUK Senior Fellowship.

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

Keywords

  • AI
  • biomarker discovery
  • dementia
  • machine learning
  • multimodal

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health

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