Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Abstract

IntroductionArtificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.MethodsWe systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.ResultsA 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.DiscussionThe 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

Artificial intelligence for dementia prevention

“…However, studies have shown ML to improve the prediction of risk of other long‐term conditions and related outcomes such as cardiovascular disease 117,118 and suicide, 119 highlighting the ML potential. ML approaches to identify risk markers or factors are already used successfully for analyses of dementia‐related neuroimaging data 71,120 . However, approaches that identify new risk markers/factors temporally preceding the health outcomes would be advantageous, due to the long prodromal period of dementia.…”

“…ML approaches to identify risk markers or factors are already used successfully for analyses of dementia-related neuroimaging data. 71,120 However, approaches that identify new risk markers/factors temporally preceding the health outcomes would be advantageous, due to the long prodromal period of dementia. Such methods have been used previously to detect early metabolite markers as risk factors for type 2 diabetes.…”

“…Together, this series provides a comprehensive overview of current applications of AI and ML to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models, 46 drug discovery and trials optimization, 47 genetics and omics, 48 biomarkers, 49 imaging, 50 prevention (this article), applied models and digital health, 51 and methods optimization 39 …”

Artificial intelligence for dementia research methods optimization

“…This series of eight articles provides a comprehensive overview of current applications of AI to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models, 7 drug discovery and trials optimization, 8 genetics and omics, 9 biomarkers, 10 neuroimaging, 11 prevention, 12 applied models and digital health, 13 and finally, this article on methods optimization.…”

Artificial intelligence for neurodegenerative experimental models

“…Together, this series provides a comprehensive overview of current applications of AI to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models (this article), drug discovery and trials optimization, 26 genetics and omics, 27 biomarkers, 28 neuroimaging, 29 prevention, 30 applied models and digital health, 31 and methods optimization. 32…”