Jove
Visualize
Contact Us

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

Herpes zoster burden in a private healthcare setting of Argentina.

Frontiers in public health·2026
Same author

Spanish validation and cross-cultural adaptation of a health recovery expectations scale.

Medicina·2026
Same author

[Difficulties in accessing colorectal cancer treatment in older adults].

Medicina·2026
Same author

Surgery with leukocyte and platelet-rich fibrin (L-PRF) vs. surgery alone for medication-related osteonecrosis of the jaw: A randomized controlled trial.

Journal of clinical and experimental dentistry·2025
Same author

Assessing the Efficacy and Safety of the Allurion® Gastric Balloon in Latin American Patients: A Multicenter Case Series.

Journal of laparoendoscopic & advanced surgical techniques. Part A·2025
Same author

[Comprehension of medical terminology in older adults: a cross-sectional study].

Medicina·2025
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 Experiment Video

Updated: Nov 4, 2025

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols
15:04

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Published on: May 20, 2016

11.2K

An Artificial Intelligence Tool for Image Simulation in Rhinoplasty.

Hernan Chinski1,2, Ricardo Lerch3, Damián Tournour2

  • 1Otolaryngology Center, Buenos Aires, CABA, Argentina.

Facial Plastic Surgery : FPS
|May 29, 2021
PubMed
Summary

An artificial intelligence model (AIM) can learn surgeon preferences for rhinoplasty simulations. While slightly less accurate than surgeon-created images, AIM simulations offer a realistic preview for patients before consultations.

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.0K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.1K

Related Experiment Videos

Last Updated: Nov 4, 2025

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols
15:04

Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

Published on: May 20, 2016

11.2K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.0K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.1K

Area of Science:

  • Plastic Surgery
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Surgeons often use computer simulations to predict rhinoplasty outcomes.
  • Artificial intelligence models (AIMs) can potentially automate this simulation process by learning surgeon-specific criteria.

Purpose of the Study:

  • To evaluate an AIM's ability to replicate a surgeon's aesthetic criteria for rhinoplasty simulations.
  • To compare the accuracy of AIM-generated simulations against surgeon-generated simulations.

Main Methods:

  • A cross-sectional survey study involving otolaryngology specialists and residents.
  • Participants evaluated randomized rhinoplasty simulations created by a surgeon and an AIM using a 7-point Likert scale.
  • Statistical analysis of agreement levels and confidence intervals.

Main Results:

  • Surgeon simulations achieved a median agreement of 6 (IQR 5-7), while AIM simulations had a median agreement of 5 (IQR 4-6) (p < 0.0001).
  • Evaluators agreed with AIM simulations 68.4% of the time (CI 64.9-71.7) and surgeon simulations 77.3% of the time (CI 74.2-80.3).

Conclusions:

  • Artificial intelligence models can emulate surgeon aesthetic criteria for generating rhinoplasty simulations.
  • AIM-generated simulations provide a realistic preview of potential rhinoplasty results for patients prior to consultations.