Harnessing the potential of machine learning and artificial intelligence for dementia research

Ranson, J. M. et al. (2023) Harnessing the potential of machine learning and artificial intelligence for dementia research. Brain Informatics, 10, 6. (doi: 10.1186/s40708-022-00183-3) (PMID:36829050) (PMCID:PMC9958222)

Abstract

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

• Item Type: Articles
• Additional Information: 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. JMR is supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL is supported by Alzheimer’s Research UK, National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, National Health and Medical Research Council (NHMRC), National Institute on Aging/National Institutes of Health (RF1AG055654), and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). NO is a UKRI Future Leaders Fellow (MR/S03546X/1) and acknowledges funding from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. AAK 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. TR is supported by the Cambridge Centre for Parkinson's Plus Disorders and the Cambridge Biomedical Research Centre. This work was additionally supported by the following funding: Alzheimer’s Research UK and Dr George Moore Endowment for Data Science at Ulster University (MB), George Henry Woolfe Legacy Fund (IFF).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lyall, Dr Donald
• Authors: Ranson, J. M., Bucholc, M., Lyall, D., Newby, D., Winchester, L., Oxtoby, N. P., Veldsman, M., Rittman, T., Marzi, S., Skene, N., Al Khleifat, A., Foote, I. F., Orgeta, V., Kormilitzin, A., Lourida, I., and Llewellyn, D. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health
• Journal Name: Brain Informatics
• Publisher: SpringerOpen
• ISSN: 2198-4018
• ISSN (Online): 2198-4026
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Brain Informatics 10: 6
• Publisher Policy: Reproduced under a Creative Commons License