Machine learning in the positron emission tomography imaging of Alzheimer's disease.

Publication/Presentation Date

9-1-2023

Abstract

The utilization of machine learning techniques in medicine has exponentially increased over the last decades due to innovations in computer processing, algorithm development, and access to big data. Applications of machine learning techniques to neuroimaging specifically have unveiled various hidden interactions, structures, and mechanisms related to various neurological disorders. One application of interest is the imaging of Alzheimer's disease, the most common cause of progressive dementia. The diagnoses of Alzheimer's disease, mild cognitive impairment, and preclinical Alzheimer's disease have been difficult. Molecular imaging, particularly via PET scans, holds tremendous value in the imaging of Alzheimer's disease. To date, many novel algorithms have been developed with great success that leverage machine learning in the context of Alzheimer's disease. This review article provides an overview of the diverse applications of machine learning to PET imaging of Alzheimer's disease.

Volume

44

Issue

9

First Page

751

Last Page

766

ISSN

1473-5628

Disciplines

Medicine and Health Sciences

PubMedID

37395538

Department(s)

Fellows and Residents

Document Type

Article

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