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

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

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...

X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

You might also read

Related Articles

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

Sort by

Same author

Sociodemographic influences on Student Mental Health and their association with activation-regulating functional impairments.

PloS one·2026

Same author

Brixia score on initial chest X-ray as a predictor of outcome in COVID-19 and H1N1 acute chest infections: A retrospective study.

Medicine·2026

Same author

Factors influencing the prediction of obstructive sleep apnea: an observational study from a developing country.

Future science OA·2026

Same author

Eye health in farmers: The impact of environmental and occupational factors.

PloS one·2025

Same author

Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.

Journal of medical systems·2025

Same author

A dataset of heart sound regurgitation of patients with heart valve disorders.

Data in brief·2025

Search research articles

Related Experiment Video

Updated: Oct 20, 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

Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks.

Natheer Khasawneh¹, Mohammad Fraiwan², Luay Fraiwan³

¹Department of Software Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

Artificial intelligence, specifically deep learning models, achieved high accuracy in detecting COVID-19 pneumonia from chest X-rays. These AI models demonstrate strong generalization capabilities on new patient data.

Keywords:

COVID-19 chest X-ray convolutional neural networks deep learning diagnosis

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Related Experiment Videos

Last Updated: Oct 20, 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

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Infectious Disease Diagnostics

Background:

The COVID-19 pandemic severely strained global healthcare systems.
Artificial intelligence (AI) offers advanced solutions for clinical diagnostics.
Accurate and rapid detection of COVID-19 pneumonia is critical.

Purpose of the Study:

To evaluate the efficacy of deep learning models for detecting COVID-19 pneumonia using chest X-rays.
To assess model performance using both local and public datasets.
To test the generalization ability of AI models on unseen data.

Main Methods:

Utilized customized and pre-trained deep learning models, specifically convolutional neural networks.
Trained and tested models on chest X-ray images from 368 confirmed COVID-19 patients and public datasets.
Employed four evaluation strategies, including combined and separate data for training and testing.

Main Results:

Achieved a high detection accuracy of 98.7% when using a combined dataset for training and testing.
Demonstrated robust performance on new, unseen data with minimal accuracy degradation.
Indicated strong generalization capabilities of the developed AI models.

Conclusions:

Deep learning models, particularly convolutional neural networks, are highly effective for COVID-19 pneumonia detection from chest X-rays.
Combining diverse datasets enhances model accuracy and reliability.
AI-powered diagnostic tools show promise in supporting clinical decision-making during pandemics.