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
Published In/Presented At
Ayubcha, C., Singh, S. B., Patel, K. H., Rahmim, A., Hasan, J., Liu, L., Werner, T., & Alavi, A. (2023). Machine learning in the positron emission tomography imaging of Alzheimer's disease. Nuclear medicine communications, 44(9), 751–766. https://doi.org/10.1097/MNM.0000000000001723
Disciplines
Medicine and Health Sciences
PubMedID
37395538
Department(s)
Fellows and Residents
Document Type
Article