The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based...
Biological aging clocks produce age estimates that can track with age-related health outcomes. This study aimed to benchmark machine learning algorithms, including regularized regression, kernel-based methods, and ensembles, for developing metabolomi...
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established met...
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monito...
With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging ...
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain...
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive...
With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between c...
The journals of gerontology. Series B, Psychological sciences and social sciences
39708298
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...
The journals of gerontology. Series A, Biological sciences and medical sciences
39909831
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...