AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Aging

Showing 71 to 80 of 387 articles

Clear Filters

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.

Neurology
BACKGROUND AND OBJECTIVES: Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential ...

Digital telomere measurement by long-read sequencing distinguishes healthy aging from disease.

Nature communications
Telomere length is an important biomarker of organismal aging and cellular replicative potential, but existing measurement methods are limited in resolution and accuracy. Here, we deploy digital telomere measurement (DTM) by nanopore sequencing to un...

Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.

Communications biology
The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseases. Cutting-edge imaging techniques and physiology yield vast high-resolution videos, demanding adva...

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology.

Genes
This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex ar...

Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

Aging
Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to elucidate key genes and pathways underlying vascular aging in diabetes using integrated bioinformatics...

nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

NeuroImage
Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI data...

A ResNet mini architecture for brain age prediction.

Scientific reports
The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuab...

The effect of head motion on brain age prediction using deep convolutional neural networks.

NeuroImage
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted ag...

Predicting the age of field mosquitoes using mass spectrometry and deep learning.

Science advances
Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of mosquitoes is key for epidemiological monitoring of malaria transmission and evaluation of vector control strategies targeting...