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

You might also read

Related Articles

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

Sort by
Same author

Disruption of CTCF binding by germline non-coding variants in <i>CDKN2B</i> suppress <i>CDKN2A</i> expression and predispose to melanoma.

medRxiv : the preprint server for health sciences·2026
Same author

Integrating virtual reality and large language models for team-based non-technical skills training and evaluation in the operating room.

npj digital surgery·2026
Same author

Prevalence of Familial Melanoma Genes and Cancer Risk Among Genomically Ascertained Individuals.

JAMA dermatology·2026
Same author

Longitudinal Changes in the Serum Pepsinogen I/II Ratio With Progression of Gastric Atrophy.

Journal of clinical laboratory analysis·2026
Same author

Nonadvanced Adenoma Number and Size as Predictors of Metachronous Advanced Colorectal Neoplasm: Implications for Surveillance Guidelines.

Gastroenterology·2026
Same author

Low-Heat and Near-Silent Pneumatic Source Driven by Integrated Endothermic-Exothermic Chemical Reactions for Soft Robots.

Soft robotics·2026

Related Experiment Video

Updated: Jul 2, 2026

Biomechanical Characterization of Human Soft Tissues Using Indentation and Tensile Testing
07:07

Biomechanical Characterization of Human Soft Tissues Using Indentation and Tensile Testing

Published on: December 13, 2016

An efficient soft tissue characterization algorithm from in vivo indentation experiments for medical simulation.

Jung Kim1, Bummo Ahn, Suvranu De

  • 1School of Mechanical, Aerospace and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. jungkim@kaist.ac.kr

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|August 30, 2008
PubMed
Summary

Researchers developed a framework to model soft tissue properties for virtual reality surgical training. This enables more realistic simulations by accurately predicting organ deformation and forces during procedures.

More Related Videos

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model
07:29

Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model

Published on: September 27, 2024

Related Experiment Videos

Last Updated: Jul 2, 2026

Biomechanical Characterization of Human Soft Tissues Using Indentation and Tensile Testing
07:07

Biomechanical Characterization of Human Soft Tissues Using Indentation and Tensile Testing

Published on: December 13, 2016

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model
07:29

Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model

Published on: September 27, 2024

Area of Science:

  • Biomedical Engineering
  • Computational Mechanics
  • Surgical Simulation

Background:

  • Realistic virtual reality (VR) surgical training requires accurate biomechanical models of soft tissues.
  • Characterizing in vivo soft tissue properties, including organ behavior, is a key challenge in developing these models.

Purpose of the Study:

  • To present an integrated framework for measuring, modeling, and calibrating the material properties of organs.
  • To provide specific parameters for modeling the behavior of pigs' intra-abdominal organs.

Main Methods:

  • Collected in vivo organ measurements from pigs using a robotic indenter and force transducer.
  • Employed a three-dimensional non-linear finite element (FE) simulation coupled with the Levenberg-Marquardt optimization algorithm to fit a constitutive model to experimental data.

Main Results:

  • An integrated framework for organ material property characterization was successfully developed.
  • Parameters for modeling pigs' intra-abdominal organs were generated through the calibrated framework.

Conclusions:

  • The calibrated mechanical models accurately compute reaction forces on surgical instruments and organ deformations.
  • These models serve as a benchmark for assessing the realism of real-time tissue models in VR surgical trainers.