Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
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.
Alzheimer's disease, artificial intelligence (AI), dementia, machine learning (ML), neurodegenerative diseases, neuroimaging
5885-5904
Borchert, Robin J.
60d1eb2d-785c-445c-a572-56f48c74b16d
Azevedo, Tiago
e4fa5c60-8d04-457b-8241-254e4548f084
Badhwar, Aman Preet
5f008d78-2354-4e0f-9923-1a8fc7d51575
Michopoulou, Sofia
f21ba2a3-f5d3-4998-801f-1ae72ff5d92c
2023
Borchert, Robin J.
60d1eb2d-785c-445c-a572-56f48c74b16d
Azevedo, Tiago
e4fa5c60-8d04-457b-8241-254e4548f084
Badhwar, Aman Preet
5f008d78-2354-4e0f-9923-1a8fc7d51575
Michopoulou, Sofia
f21ba2a3-f5d3-4998-801f-1ae72ff5d92c
Borchert, Robin J., Azevedo, Tiago and Badhwar, Aman Preet
,
et al.
(2023)
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review.
Alzheimer's and Dementia, 19 (12), .
(doi:10.1002/alz.13412).
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.
Text
Alzheimer s Dementia - 2023 - Borchert - Artificial intelligence for diagnostic and prognostic neuroimaging in dementia
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Published date: 2023
Additional Information:
Funding information: Alzheimer’s Research UK; National Institute for Health and Care Research (NIHR); National Institute for Health Research (NIHR); Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council, Grant/Award Number: EP/N510129/1; Medical Research Council, Grant/Award Number: MR/X005674/1; National Health and Medical Research Council (NHMRC); National Institute on Aging/National Institutes of Health, Grant/Award Number: RF1AG055654
Keywords:
Alzheimer's disease, artificial intelligence (AI), dementia, machine learning (ML), neurodegenerative diseases, neuroimaging
Identifiers
Local EPrints ID: 487948
URI: http://eprints.soton.ac.uk/id/eprint/487948
ISSN: 1552-5260
PURE UUID: 878e8745-0cd1-43c3-9544-17e74ecd5bd4
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Date deposited: 11 Mar 2024 17:38
Last modified: 17 Mar 2024 07:57
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Contributors
Author:
Robin J. Borchert
Author:
Tiago Azevedo
Author:
Aman Preet Badhwar
Author:
Sofia Michopoulou
Corporate Author: et al.
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