Robin J. Borchert
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review
Borchert, Robin J.; Azevedo, Tiago; Badhwar, AmanPreet; Bernal, Jose; Betts, Matthew; Bruffaerts, Rose; Burkhart, Michael C.; Dewachter, Ilse; Gellersen, Helena M.; Low, Audrey; Lourida, Ilianna; Machado, Luiza; Madan, Christopher R.; Malpetti, Maura; Mejia, Jhony; Michopoulou, Sofia; Muñoz‐Neira, Carlos; Pepys, Jack; Peres, Marion; Phillips, Veronica; Ramanan, Siddharth; Tamburin, Stefano; Tantiangco, Hanz M.; Thakur, Lokendra; Tomassini, Alessandro; Vipin, Ashwati; Tang, Eugene; Newby, Danielle; Ranson, Janice M.; Llewellyn, David J.; Veldsman, Michele; Rittman, Timothy
Authors
Tiago Azevedo
AmanPreet Badhwar
Jose Bernal
Matthew Betts
Rose Bruffaerts
Michael C. Burkhart
Ilse Dewachter
Helena M. Gellersen
Audrey Low
Ilianna Lourida
Luiza Machado
Dr CHRISTOPHER MADAN CHRISTOPHER.MADAN@NOTTINGHAM.AC.UK
ASSISTANT PROFESSOR
Maura Malpetti
Jhony Mejia
Sofia Michopoulou
Carlos Muñoz‐Neira
Jack Pepys
Marion Peres
Veronica Phillips
Siddharth Ramanan
Stefano Tamburin
Hanz M. Tantiangco
Lokendra Thakur
Alessandro Tomassini
Ashwati Vipin
Eugene Tang
Danielle Newby
Janice M. Ranson
David J. Llewellyn
Michele Veldsman
Timothy Rittman
Abstract
Introduction
Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.
Methods
We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.
Results
A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.
Discussion
The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.
Highlights
There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Citation
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., Lourida, I., Machado, L., Madan, C. R., Malpetti, M., Mejia, J., Michopoulou, S., Muñoz‐Neira, C., Pepys, J., Peres, M., Phillips, V., …Rittman, T. (2023). Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 19(12), 5885-5904. https://doi.org/10.1002/alz.13412
Journal Article Type | Review |
---|---|
Acceptance Date | Jun 2, 2023 |
Online Publication Date | Aug 10, 2023 |
Publication Date | 2023-12 |
Deposit Date | Sep 26, 2023 |
Publicly Available Date | Oct 11, 2023 |
Print ISSN | 1552-5260 |
Electronic ISSN | 1552-5279 |
Publisher | Wiley |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 12 |
Pages | 5885-5904 |
DOI | https://doi.org/10.1002/alz.13412 |
Keywords | Alzheimer's disease, neuroimaging, machine learning (ML), dementia, neurodegenerative diseases, artificial intelligence (AI) |
Public URL | https://nottingham-repository.worktribe.com/output/24413883 |
Publisher URL | https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.13412 |
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Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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