AIMC Topic: Aging

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EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Schizophrenia bulletin
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...

Ethics From the Outset: Incorporating Ethical Considerations into the Artificial Intelligence and Technology Collaboratories for Aging Research Pilot Projects.

The journals of gerontology. Series A, Biological sciences and medical sciences
There is an urgent need to develop tools to enable older adults to live healthy, independent lives for as long as possible. To address this need, the National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITCs) for...

Machine Learning Scoring Reveals Increased Frequency of Falls Proximal to Death in Drosophila melanogaster.

The journals of gerontology. Series A, Biological sciences and medical sciences
Falls are a significant cause of human disability and death. Risk factors include normal aging, neurodegenerative disease, and sarcopenia. Drosophila melanogaster is a powerful model for study of normal aging and for modeling human neurodegenerative ...

Engagement of Older Adults in the Design, Implementation, and Evaluation of Artificial Intelligence Systems for Aging: A Scoping Review.

The journals of gerontology. Series A, Biological sciences and medical sciences
Integration of artificial intelligence (AI) in health and healthcare, especially for older adults, has significantly advanced healthcare delivery. AI technologies, with capabilities such as self-learning and pattern recognition, are employed to addre...

Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.

Human brain mapping
Compared to nonverbal cognition such as executive or memory functions, language-related cognition generally appears to remain more stable until later in life. Nevertheless, different language-related processes, for example, verbal fluency versus voca...

Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.

The journals of gerontology. Series B, Psychological sciences and social sciences
OBJECTIVES: This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and me...

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

European heart journal
BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronolog...

A deep-learning retinal aging biomarker for cognitive decline and incident dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

Briefings in functional genomics
The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change ...

Co-creating Humanistic AI AgeTech to Support Dynamic Care Ecosystems: A Preliminary Guiding Model.

The Gerontologist
As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to support older adults (e.g., smart homes, assistive robots, wheelchairs) are increasingly applying art...