Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray

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

MMP9 Genotype and Systemic T-Cell Subsets Correlate With Structural and Functional Outcomes in Neovascular Age-Related Macular Degeneration.

Investigative ophthalmology & visual science·2026

Same author

Diagnostic accuracy of ultrasound compared to magnetic resonance imaging for rotator cuff tears: a systematic review.

International journal of burns and trauma·2026

Same author

Automatic assessment of lung involvement in systemic sclerosis using deep learning.

Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences·2026

Same author

Loss of Enhancement in Vestibular Schwannomas Post Stereotactic Radiosurgery: Impact on Tumor Control and Serviceable Hearing: Erratum.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology·2026

Same author

Neurobrucellosis presenting as acute stroke with brain abscesses: a case report.

American journal of neurodegenerative disease·2026

Same author

Comparative Evaluation of Deep Learning Models for 3D Segmentation and Volumetry of Vestibular Schwannomas Using Large Heterogeneous Data Sets with External Validation.

AJNR. American journal of neuroradiology·2025

Search research articles

Related Experiment Video

Updated: Dec 12, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.

Shervin Minaee¹, Rahele Kafieh², Milan Sonka³

¹Snap Inc., Seattle, WA, USA.

Medical Image Analysis

|August 12, 2020

Summary

Deep learning models accurately detect COVID-19 from chest X-rays, achieving 98% sensitivity. This rapid diagnostic approach aids early patient care during the pandemic.

Keywords:

COVID-19 Deep learning Transfer learning X-ray imaging

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Dec 12, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Chest radiography is a key method for identifying COVID-19 related abnormalities.
Early detection is crucial for patient management and limiting disease spread.

Purpose of the Study:

To investigate the efficacy of deep learning models for detecting COVID-19 from chest X-ray images.
To evaluate the performance of popular convolutional neural networks (CNNs) in identifying COVID-19 indicators.

Main Methods:

A dataset of 5000 chest X-rays was curated, with COVID-19 cases identified by radiologists.
Transfer learning was applied to train ResNet18, ResNet50, SqueezeNet, and DenseNet-121 models.
Model performance was evaluated using sensitivity, specificity, ROC curves, and heatmaps.

Main Results:

Most trained CNN models achieved a sensitivity of 98% (±3%) and a specificity of approximately 90%.
Generated heatmaps highlighted potentially infected lung regions, correlating with radiologist annotations.
The study provides a publicly available dataset and model implementations.

Conclusions:

Deep learning models demonstrate high potential for accurate and rapid COVID-19 detection using chest X-rays.
Further validation on larger datasets is recommended for robust accuracy assessment.
The developed tools can support clinical decision-making in pandemic scenarios.