AIMC Topic: Aging

Clear Filters Showing 211 to 220 of 489 articles

Nuclear morphology is a deep learning biomarker of cellular senescence.

Nature aging
Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear mo...

Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry.

Cells
Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensi...

Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue ...

A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery.

Surgery today
A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction ...

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain.

NeuroImage
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Re...

MetaAge: Meta-Learning Personalized Age Estimators.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods ...

XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging.

IEEE journal of biomedical and health informatics
The purpose of this study was to confirm the potential of XGBoost as a vascular aging assessment model based on the photoplethysmogram (PPG) features suggested in previous studies, and to explore the key PPG features for vascular aging assessment thr...

Management and Analysis of Sports Health Level of the Elderly Based on Deep Learning.

Computational intelligence and neuroscience
With the accelerating rate of population aging in China, the health of the elderly has received more and more attention and has become one of the most important issues in the elderly care industry. Because of insufficient research on the personal hea...

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review.

Advances in wound care
As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at prov...

Application of Imaging Examination Based on Deep Learning in the Diagnosis of Viral Senile Pneumonia.

Contrast media & molecular imaging
Medical image classification technology, preferably which is based on the deep learning, is not only a key auxiliary diagnosis and treatment method in clinical medicine but also an important direction of scientific research. With the intensification ...