Modat, Marc;
Cash, David;
Dos Santos Canas, Liane;
Bocchetta, Martina;
Ourselin, Sébastien;
(2023)
Machine Learning for Alzheimer’s Disease and Related Dementias.
In: Colliot, Oliver, (ed.)
Machine Learning for Brain Disorders.
(pp. 807-846).
Humana: New York, NY, USA.
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Abstract
Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.
| Type: | Book chapter |
|---|---|
| Title: | Machine Learning for Alzheimer’s Disease and Related Dementias |
| ISBN: | 1071631950 |
| ISBN-13: | 9781071631959 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-1-0716-3195-9_25 |
| Publisher version: | https://doi.org/10.1007/978-1-0716-3195-9_25 |
| Language: | English |
| Additional information: | Open Access This chapter is licensed under the term s of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as y ou give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licens e and indicate if changes were made. The images or other third party material in this chapter are included in t he chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is n ot included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regula tion or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Keywords: | Dementia, Alzheimer’s disease, Cognitive impairment, Machine learning, Data harmonization, Biomarkers, Imaging, Classification, Disease progression modeling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174537 |
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