Artificial intelligence for dementia prevention

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

“…Together, this series provides a comprehensive overview of current applications of artificial intelligence (AI) to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models 28 , drug discovery and trials optimization 29 , genetics and omics 30 , biomarkers 31 , neuroimaging (this article), prevention 32 , applied models and digital health 33 , and methods optimization 34 .…”

Section: Introductionmentioning

confidence: 99%

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

Borchert

Azevedo

Badhwar

et al. 2023

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

“…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…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for neurodegenerative experimental models

Marzi,

Schilder,

Nott

et al. 2023

INTRODUCTIONExperimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.METHODSHere we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.RESULTSConsidering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross‐model reproducibility and translation to human biology, while sustaining biological interpretability.DISCUSSIONAI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.Highlights There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross‐species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi‐omics analysis with AI offers exciting future possibilities in drug discovery.