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

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.4K

You might also read

Related Articles

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

Sort by
Same author

A flap mechanics testbed for skin reconstructive surgery: Evaluating the mechanical interaction between flap design and anisotropy.

Acta biomaterialia·2026
Same author

Clinical Approaches to the Diagnosis and Treatment of Vascular Anomalies.

Plastic and reconstructive surgery·2026
Same author

A Blind Spot in the Algorithm: Assessing Bias in Artificial Intelligence-Generated Images of Plastic Surgery Patients.

Plastic and reconstructive surgery. Global open·2026
Same author

Bayesian Aneurysm Growth Detection via Surface Displacement Modeling.

ArXiv·2026
Same author

Imposter Phenomenon or Perfectionism? Imposter Syndrome and Perfectionism in Plastic Surgery Training and Practice.

Plastic and reconstructive surgery. Global open·2026
Same author

Underrepresentation in Surgical Societies: A Specialty-Focused Analysis of ACS and SUS Membership.

The American surgeon·2026
Same journal

A 3D digital image correlation framework for skin strain analysis with application to the peripheral intravenous catheter interface.

Journal of the mechanical behavior of biomedical materials·2026
Same journal

A biofidelic in vitro model to determine the tribological behaviour of mucous layers and their influence on tissue damage.

Journal of the mechanical behavior of biomedical materials·2026
Same journal

Mechanical behaviour of triaxial flat braided soft tissue repair devices.

Journal of the mechanical behavior of biomedical materials·2026
Same journal

Hybrid III lumbar spinal column injury risk curves from vertical impact.

Journal of the mechanical behavior of biomedical materials·2026
Same journal

Evaluation of the remineralisation potential of a novel peptide GA-C16G2 on artificial root dentine lesions.

Journal of the mechanical behavior of biomedical materials·2026
Same journal

Multifrequency tabletop magnetic resonance elastography for ex-vivo characterization of murine intestinal tissue biomechanics.

Journal of the mechanical behavior of biomedical materials·2026
See all related articles

Related Experiment Video

Updated: Nov 11, 2025

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.3K

Improving reconstructive surgery design using Gaussian process surrogates to capture material behavior uncertainty.

Casey Stowers1, Taeksang Lee1, Ilias Bilionis1

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.

Journal of the Mechanical Behavior of Biomedical Materials
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

Surgeons can optimize flap orientation to minimize surgical stress and complications. This study uses Gaussian process models to find optimal fiber directions for common reconstructive flaps, improving patient outcomes.

Keywords:
Local flapsMachine learningNonlinear finite elementsSkin biomechanicsSoft tissue mechanics

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.4K
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.7K

Related Experiment Videos

Last Updated: Nov 11, 2025

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.3K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.4K
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

6.7K

Area of Science:

  • Biomedical Engineering
  • Computational Mechanics
  • Plastic Surgery

Background:

  • Minimizing stress in reconstructive surgery is crucial for functional and aesthetic outcomes, but stress measurement in the operating room is challenging.
  • Current surgical planning relies on surgeon experience, lacking precise predictive tools for stress and strain.
  • Finite element (FE) simulations show promise for predicting stress but are computationally intensive.

Purpose of the Study:

  • To develop computationally efficient Gaussian process (GP) surrogate models for predicting stress and strain in cutaneous flaps.
  • To identify the most impactful material parameter influencing strain variations in different flap types.
  • To optimize flap orientation based on clinical guidelines to reduce surgical stress and potential complications.

Main Methods:

  • Developed Gaussian process (GP) surrogate models using a limited number of finite element (FE) simulations for advancement, rotation, and transposition flaps.
  • Performed global sensitivity analysis on the GP surrogates to determine the influence of material parameters, particularly fiber direction.
  • Conducted an optimization analysis to find optimal fiber directions for each flap type, considering clinical objectives and material property uncertainties.

Main Results:

  • Gaussian process (GP) surrogate models were successfully created for three common flap types, offering computationally efficient stress and strain predictions.
  • Global sensitivity analysis revealed that fiber direction is the most significant factor affecting strain field variations in flaps.
  • Optimal flap orientations were proposed for three distinct clinical objectives, demonstrating the potential to reduce stress and associated complications.

Conclusions:

  • Gaussian process surrogate models provide an efficient alternative to computationally expensive FE simulations for surgical planning.
  • Optimizing flap orientation with respect to underlying fiber direction is a clinically controllable factor that can significantly reduce surgical stress.
  • The proposed optimal flap orientations can guide surgeons in planning procedures to minimize complications and improve reconstructive surgery outcomes.