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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

9.2K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Safety and Feasibility of a Novel Split Design Left Atrial Appendage Occluder: From Preclinical Investigations to First-in-Human Application.

Cardiovascular therapeutics·2026
Same author

Small Orifice Regurgitation Increasing Phenomenon During Transcatheter Edge-to-Edge Repair: Case Series.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2026
Same author

Current Advances in Epidemiology, Diagnosis, and Management of TAVR-Associated Infective Endocarditis: A Narrative Review.

Reviews in cardiovascular medicine·2026
Same author

Serum Proteomic Profiling Uncovers LGALS3BP as a Potential Biomarker for Idiopathic Pulmonary Arterial Hypertension.

The clinical respiratory journal·2025
Same author

Hemodynamic Variability in Aortic Stenosis and Regurgitation During Transcatheter Aortic Valve Replacement With Self-Expanding Valves.

Cardiovascular therapeutics·2025
Same author

Single Fluoroscopy Versus Transesophageal Echocardiogram: A Comparative Evaluation for Left Atrial Appendage Occlusion With LACbes Device.

Cardiovascular therapeutics·2025
Same journal

The Immediate Impact of Infarct Size on the Systemic Inflammatory Response: IL-6 as Central Mediator Identified through Biomarker and Proteomic Profiling.

Journal of cardiovascular translational research·2026
Same journal

Extracellular Vesicles Link Cerebral Ischemia to Coronary Microvascular Dysfunction - Role for RGD Motif-Activated Endothelin Signaling.

Journal of cardiovascular translational research·2026
Same journal

Tracing the pathogenic PLN p.(Arg14del) variant across the globe; more than just a local curiosity.

Journal of cardiovascular translational research·2026
Same journal

Immunometabolic Remodeling in Ischemic and Non-Ischemic Heart Failure.

Journal of cardiovascular translational research·2026
Same journal

Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.

Journal of cardiovascular translational research·2026
Same journal

Pim1 Mitigates Heart Failure by Suppressing Ferroptosis Via Activation of the mTORC1/SLC7A11/GPX4 Axis.

Journal of cardiovascular translational research·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
06:59

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

10.6K

Force Analysis Using Self-Expandable Valve Fluoroscopic Imaging: a way Through Artificial Intelligence.

Yiming Qi1,2, Xiaochun Zhang1,2, Zhiyun Shen3

  • 1Department of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.

Journal of Cardiovascular Translational Research
|August 1, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning model to analyze force distribution in transcatheter aortic valve replacement (TAVR) stents using fluoroscopic images. This approach enhances understanding of TAVR complications by correlating images with mechanical stress.

Keywords:
Artificial intelligenceFluoroscopy imageMechanical distributionSelf-expandable valve

More Related Videos

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

2.9K
In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
11:16

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Published on: February 25, 2022

3.3K

Related Experiment Videos

Last Updated: May 5, 2026

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
06:59

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Published on: June 3, 2018

10.6K
Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

2.9K
In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
11:16

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging

Published on: February 25, 2022

3.3K

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Mechanics

Background:

  • Transcatheter aortic valve replacement (TAVR) involves implanting self-expandable valves.
  • Understanding the mechanical forces and stress distribution on these valves is crucial for predicting and mitigating complications.
  • Current methods for analyzing valve mechanics during TAVR may not fully capture the complex interactions.

Purpose of the Study:

  • To develop a novel force analysis model that correlates fluoroscopic images of self-expandable valves with their stress distribution.
  • To investigate the potential of machine learning in analyzing mechanical information from valve fluorescence images.
  • To enhance the understanding of TAVR complications through improved mechanical analysis.

Main Methods:

  • A nonmetallic measuring device was manufactured to apply controlled forces to valve stents and measure force magnitude.
  • 465 sets of fluorescent films were obtained under various force conditions, generating 5580 images and corresponding force data.
  • A mechanical analysis model, XrayGLM, was trained using valve fluorescence images for force distribution analysis.

Main Results:

  • The developed XrayGLM model achieved an approximate accuracy of 70% (range: 50-88.3%) in image-based force analysis.
  • A high relative accuracy of 93.3% (range: 75-100%) was observed in the force distribution analysis.
  • Fluoroscopic images of TAVR valve stents were confirmed to contain significant mechanical information.

Conclusions:

  • Fluoroscopic images of TAVR valve stents contain valuable mechanical data.
  • Machine learning models can effectively learn the relationship between stent images and force distribution.
  • This approach offers a promising method for enhancing the understanding and prediction of TAVR complications.