Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same author

Longitudinal Mammographic Breast Density Changes and Associated Factors in Older Korean Women.

Radiology. Imaging cancer·2026
Same author

Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.

European radiology·2026
Same author

Chest Radiograph-Derived Age Acceleration as an Early Marker of Pulmonary Dysfunction in Middle-Aged Asian Adults.

Chest·2026
Same author

Response to "Enhancing the Study of Long-Term Pulmonary Sequelae After COVID-19: Methodological Perspectives on Imaging and Clinical Integration".

Korean journal of radiology·2026
Same author

Human-in-the-Loop Large Language Model-Augmented Diagnostic Reasoning in Thoracic Imaging: Impact of Radiologic Expertise.

AJR. American journal of roentgenology·2026
Same journal

Externally Tested AI for Lung Nodule Classification: A Realistic Benchmark for an Emerging Screening Era.

Radiology. Artificial intelligence·2026
Same journal

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Radiology. Artificial intelligence·2026
Same journal

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Radiology. Artificial intelligence·2026
Same journal

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026
Same journal

When One Sequence Is Enough-And When It Isn't.

Radiology. Artificial intelligence·2026
Same journal

Cracking the Registration Conundrum in Breast MRI: Preserving the Tumor Signal to Reveal True Treatment Change.

Radiology. Artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
09:10

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

851

Accelerated Aging and Aging Velocity from Deep Learning-based Chest Radiograph-derived Age for Predicting

Yoosoo Chang1,2,3, Hyungjin Kim4, Seungho Lee4

  • 1Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Radiology. Artificial Intelligence
|April 1, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning-based radiographic age and aging velocity predict mortality risk in Asian adults. Accelerated aging and faster aging velocity are linked to increased all-cause and cause-specific mortality, especially in females.

Keywords:
Conventional RadiographyConvolutional Neural NetworkEpidemiologyThorax

Related Experiment Videos

Last Updated: Jun 12, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
09:10

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

851

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Gerontology

Background:

  • Chronological age is a primary risk factor for mortality.
  • Assessing biological age through medical imaging offers potential prognostic insights.
  • Deep learning models can estimate biological age from radiographic data.

Purpose of the Study:

  • To evaluate the prognostic capability of deep learning-derived radiographic age and aging velocity for predicting mortality in an Asian population.
  • To investigate the association between accelerated aging and mortality risk.
  • To determine if aging velocity predicts mortality independently of baseline characteristics.

Main Methods:

  • Retrospective cohort study of 421,894 Korean adults with chest radiography data (2006-2020).
  • Radiographic age estimated using the AgeNet deep learning model.
  • Accelerated aging defined as radiographic age ≥ 5 years older than chronological age.
  • Aging velocity calculated from serial radiographs.
  • Cox and Fine-Gray models used for mortality prediction.

Main Results:

  • Accelerated aging was significantly associated with increased all-cause and cause-specific mortality (HRs 1.26 for males, 1.52 for females).
  • Aging velocity independently predicted mortality (cumulative mortality ratio per 1-SD increase: 1.24 for males, 1.35 for females).
  • Accelerated aging velocity (≥ 1.5 years/year) significantly increased mortality risk (MRRs 1.51 for males, 1.71 for females).

Conclusions:

  • Deep learning-derived radiographic age and aging velocity are independent predictors of all-cause and cause-specific mortality.
  • Accelerated aging and faster aging velocity indicate higher mortality risk.
  • Findings highlight the potential of radiographic age as a prognostic biomarker.