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Ageing and Alzheimer’s Disease

Application of Artificial Intelligence in Mechanistic Studies, Diagnosis, and Drug Development

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Artificial Intelligence in Medicine

Abstract

Artificial intelligence (AI) implies the use of a machine with limited human interference to model intelligent actions. It covers a broad range of research studies from machine intelligence for computer vision, robotics, and natural language processing to more theoretical machine learning algorithm design and, recently, “deep learning” development. The application of AI in medical fields is booming, including the use of AI in data collection, analysis, mechanistic prediction, to clinical disease diagnosis and drug development. In this chapter, we focus on the challenges in the studies of aging and the age-predisposed Alzheimer’s disease (AD) and summarize on how to use AI to help addressing these questions. We finally provide future perspectives on the use of AI in aging research and AD.

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Acknowledgments

The authors acknowledge the valuable work of the many investigators whose published articles they were unable to cite owing to space limitations. The authors thank Dr. Chenglong Xie for discussion and Thale Dawn Patrick-Brown for reading the manuscript. E.F.F. was supported by HELSE SØR-ØST (#2017056, #2020001, #2021021), the Research Council of Norway (#262175 and #277813), the National Natural Science Foundation of China (#81971327), Akershus University Hospital (#269901, #261973), the Civitan Norges Forskningsfond for Alzheimers sykdom (for a 3-year Ph.D. fellowship, #281931), the Czech Republic-Norway KAPPA programme (with Martin Vyhnálek, #TO01000215), and the Rosa sløyfe/Norwegian Cancer Society & Norwegian Breast Cancer Society (#207819). G.Y. was supported in part by the British Heart Foundation (Project Number: PG/16/78/32402), in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology), in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combatting Coronavirus Infections Award “DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics” (H2020-JTI-IMI2 101005122), in part by the AI for Health Imaging Award “CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management” (H2020-SC1-FA-DTS-2019-1 952172), and in part by the UK Research and Innovation (MR/V023799/1). A.R. was also funded by China Scholarship Council [http://www.csc.edu.cn/]; the funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Declaration of Interests

E.F.F. has CRADA arrangements with ChromaDex. E.F.F. and G.Y. are consultants to Aladdin Healthcare Technologies. E.F.F. is a consultant to the Vancouver Dementia Prevention Centre and Intellectual Labs. Z.N, X.J and B.T are affiliated with MindRank AI ltd.

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Authors and Affiliations

  1. Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway

    Ruixue Ai & Evandro F. Fang

  2. MindRank AI Ltd., Hangzhou, Zhejiang, China

    Xurui Jin, Bowen Tang & Zhangming Niu

  3. Cardiovascular Research Centre, Royal Brompton Hospital, London, UK

    Guang Yang

  4. National Heart and Lung Institute, Imperial College London, London, UK

    Guang Yang

  5. Aladdin Healthcare Technologies ltd., London, UK

    Zhangming Niu

  6. The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway

    Evandro F. Fang

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  1. Ruixue Ai
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  2. Xurui Jin
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  3. Bowen Tang
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  4. Guang Yang
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  5. Zhangming Niu
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  6. Evandro F. Fang
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Correspondence to Evandro F. Fang .

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Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Ai, R., Jin, X., Tang, B., Yang, G., Niu, Z., Fang, E.F. (2021). Ageing and Alzheimer’s Disease. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_74-1

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