Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
Highlights
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease that progressively destroys cognitive skills, up to the development of dementia
We focus on recent findings using Artificial Intelligence (AI) for AD research and future challenges awaiting its application: Will it be possible to make an early diagnosis of AD with AI? Will AI be able to predict conversion from Mild Cognitive Impairment (MCI) to AD dementia, stratify patients, and identify “malignant” forms with worse disease progression? will AI be able to predict the course and progression of the disease and help in finding a cure for AD?
Some important data-sharing resources are the Alzheimer’s Disease Genetics Consortium (ADGC, www.adgenetics.org, accessed on 30 May 2021), Alzheimer’s Disease Sequencing Project (ADSP, www.niagads.org/adsp/content/home accessed on 30 May 2021), Alzheimer’s Disease Neuroimaging Initiative (ADNI, http: //adni.loni.usc.edu/ accessed on 30 May 2021), AlzGene, Dementias Platform UK (DPUK, https://portal.dementiasplatform.uk/ accessed on 30 May 2021), Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, www.niagads.org/ accessed on 30 May 2021), Global Alzheimer’s Association Interactive Network (GAAIN, www.gaain.org/ accessed on 30 May 2021), and National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD, https://ncrad.iu.edu accessed on 30 May 2021) [33,34]
Summary
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease that progressively destroys cognitive skills, up to the development of dementia. The occurrence of co-existing pathologies is a common feature in those cases of neurodegenerative diseases that share a common pathogenic mechanism, consisting of extracellular and/or intracellular insoluble fibril aggregates of abnormal misfolded proteins (e.g., the formation of amyloid plaques, tau tangles, or α-synuclein inclusions) In this context, the system biology approach, which aims at the integration of clinical and multi-omics data, can help to detect and recognize the pathophysiological and molecular changes characteristic of AD or other pathologies, as well as the associated clinical manifestations occurring, in particular, in the pre-clinical stages [8]. Formalized domain expertise from psychology, neuroscience, neurology, psychiatry, geriatric medicine, biology, and genetics can be integrated with novel analytic approaches from bioinformatics and statistics to be applied on Big Data in AD research projects, with the aim of answering detailed questions through the use of predictive models These can succeed in answering key questions about promising biomarker combinations, patient sub-groups, and disease progression, leading to the development of effective treatment strategies, helping patients with tailored medical approaches [18]. We focus on recent findings using AI for AD research and future challenges awaiting its application: Will it be possible to make an early diagnosis of AD with AI? Will AI be able to predict conversion from Mild Cognitive Impairment (MCI) to AD dementia, stratify patients, and identify “malignant” forms with worse disease progression? will AI be able to predict the course and progression of the disease and help in finding a cure for AD?
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