Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

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INTRODUCTIONA wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.METHODSML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.RESULTSRisk‐profiling tools may help identify high‐risk populations for clinical trials; however, their performance needs improvement. New risk‐profiling and trial‐recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug‐repurposing efforts and prioritization of disease‐modifying therapeutics.DISCUSSIONML is not yet widely used but has considerable potential to enhance precision in dementia prevention.Highlights Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk‐profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk‐management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Section: Introduction To Dementia Prevention Artificial Intelligence ...mentioning

confidence: 99%

Artificial intelligence for dementia prevention

Newby,

Orgeta,

Marshall

et al. 2023

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

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.

“…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