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

Observational Learning01:12

Observational Learning

188
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
188
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.4K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.4K

You might also read

Related Articles

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

Sort by
Same author

Evaluation of Machine Learning and Statistical Models for Predicting Long Term Gastrostomy Tube Dependency in Patients Undergoing Major Oral Cavity Cancer Surgery With Free Flap Reconstruction.

Head & neck·2026
Same author

Reviewing the Safety of Providing Out of Operating Room Anesthesia for Acute Burns in Pediatric Patients.

Journal of burn care & research : official publication of the American Burn Association·2026
Same author

An interprofessional pediatric procedural sedation service: development, pilot testing, and implementation.

Journal of anesthesia·2025
Same author

High-fidelity measurement of pulse arrival time in critically ill children using standard bedside monitoring equipment.

Physiological measurement·2025
Same author

A Novel Combined Surgical and Interventional Radiology Vascular Reconstruction, With Stenting of Meso-Rex Bypass, to Successfully Manage Recurrent Portal Venous Flow Obstruction Post Segmental Liver Transplant in a Pediatric Patient.

Pediatric transplantation·2025
Same author

Innovative Mobile App (CPD By the Minute) for Continuing Professional Development in Medicine: Multimethods Study.

JMIR medical education·2025

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Deep Learning Model for Automated Trainee Assessment During High-Fidelity Simulation.

Asad Siddiqui1, Zhoujie Zhao2, Chuer Pan3

  • 1A. Siddiqui is a pediatric anesthesiologist and assistant professor, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.

Academic Medicine : Journal of the Association of American Medical Colleges
|October 26, 2023
PubMed
Summary

This study developed a deep learning model to automatically assess anesthesiology trainees in simulated critical events. The model achieved 71% accuracy, showing promise for improving simulation-based medical education assessments.

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Anesthesiology Training

Background:

  • Competency-based medical education requires frequent trainee assessments.
  • Simulation is a valuable tool but faces limitations in examiner access, cost, and reliability.
  • Automated assessment tools can enhance the accessibility and quality of simulation-based evaluations.

Purpose of the Study:

  • To develop and validate a deep learning model for automated pass/fail assessment of anesthesiology trainees.
  • To address the limitations of manual assessment in simulation-based training.
  • To improve the efficiency and consistency of evaluating critical event performance.

Main Methods:

  • Retrospective analysis of 52 anaphylaxis simulation videos.
  • Development and validation of a deep learning model using a bidirectional transformer encoder.
  • Training and testing the model on a dataset of simulated critical event videos.

Main Results:

  • The strongest deep learning model achieved 71% accuracy and an F1 score of 0.68.
  • The model demonstrated feasibility in assessing trainee performance in a simulated anaphylaxis scenario.
  • Evaluation metrics included F1 score, accuracy, recall, and precision.

Conclusions:

  • Deep learning models can be feasibly developed for automated assessment of medical trainees in simulations.
  • Further research with larger datasets and diverse simulations is needed to improve model accuracy.
  • This approach has significant implications for the future of medical education and performance assessment.