AIMC Topic: Aging
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Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.
Journal of the European Academy of Dermatology and Venereology : JEADV
Oct 3, 2024
BACKGROUND: Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicabili...
Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study.
The lancet. Healthy longevity
Sep 30, 2024
BACKGROUND: Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge here...
Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences.
Heart rhythm
Sep 27, 2024
BACKGROUND: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events.
The revolution in high-throughput proteomics and AI.
Science (New York, N.Y.)
Sep 26, 2024
The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to me...
Is Artificial Intelligence ageist?
European geriatric medicine
Sep 25, 2024
INTRODUCTION: Generative Artificial Intelligence (AI) is a technological innovation with wide applicability in daily life, which could help elderly people. However, it raises potential conflicts, such as biases, omissions and errors.
Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.
Informatics for health & social care
Sep 24, 2024
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...
Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.
Mechanisms of ageing and development
Sep 14, 2024
The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and ...
Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.
Aging
Sep 10, 2024
Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with a...
Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.
The Journal of investigative dermatology
Sep 3, 2024
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...
Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging.
Journal of cachexia, sarcopenia and muscle
Aug 29, 2024
BACKGROUND: Sarcopenia is an age-related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1. This study ...
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