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

X-ray Imaging01:24

X-ray Imaging

5.5K
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...
5.5K
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

3.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
3.8K
X-ray Crystallography02:18

X-ray Crystallography

23.9K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
23.9K

You might also read

Related Articles

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

Sort by
Same author

Biodegradable Self-Powered Electrotherapy Patch for Integrated Smart Wound Management.

Analytical chemistry·2026
Same author

Therapeutic potential of glycyrrhizic acid in inflammation-related diseases: from HMGB1-oriented molecular insights to preclinical application.

Archives of pharmacal research·2026
Same author

CT-override: endoscopic updates to preoperative anatomical models during ablative surgery.

International journal of computer assisted radiology and surgery·2026
Same author

Shape Sensing of Continuum Manipulators with Fiber Bragg Grating Sensor Arrays: Accounting for Actuator Velocity Effects.

IEEE sensors journal·2026
Same author

BronchOpt: vision-based pose optimization with fine-tuned foundation models for accurate bronchoscopy navigation.

International journal of computer assisted radiology and surgery·2026
Same author

Wearable Battery-Free Electrotherapy of Smartsensors for Wound Healthcare.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Related Experiment Video

Updated: Jun 29, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.

Cong Gao1, Benjamin D Killeen1, Yicheng Hu1

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Nature Machine Intelligence
|March 25, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can now analyze medical images using simulated data. Training AI on synthesized X-ray images (SyntheX) achieves performance comparable to real data, accelerating development.

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

6.9K

Related Experiment Videos

Last Updated: Jun 29, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.4K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

6.9K

Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Machine learning

Background:

  • Automated interpretation of medical images using AI is advancing.
  • AI application in interventional image analysis is limited by data collection challenges.
  • Limitations include ethical concerns, cost, scalability, data integrity, and lack of ground truth.

Purpose of the Study:

  • To explore the viability of using realistic simulated images for training AI in interventional image analysis.
  • To develop a model transfer paradigm for X-ray image analysis using synthesized data.
  • To overcome limitations of real-world data collection in medical AI development.

Main Methods:

  • Creation of realistic simulated human models for generating synthetic medical images.
  • Training AI image analysis models on synthesized data combined with domain generalization techniques.
  • Development of the SyntheX model transfer paradigm for X-ray image analysis.

Main Results:

  • AI models trained on synthesized data performed comparably to models trained on real data.
  • The SyntheX paradigm demonstrated effective X-ray image analysis.
  • SyntheX-trained models sometimes outperformed real-data-trained models due to larger dataset effectiveness.

Conclusions:

  • Realistic simulated images offer a viable alternative to large-scale in situ data collection for AI training.
  • SyntheX can accelerate the development and evaluation of AI-based X-ray systems.
  • This approach facilitates testing new instrumentation and surgical techniques without ethical or practical data constraints.