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Researchers developed a checklist to standardize reporting for virtual reality surgical simulation studies using machine learning. This tool aims to improve interdisciplinary communication and knowledge transfer in surgical education.

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Area of Science:

  • Computer Science, Medicine, and Education
  • Surgical Simulation and Machine Learning
  • Psychomotor Performance Assessment

Background:

  • Virtual reality (VR) simulators generate extensive data for machine learning (ML) analysis, advancing psychomotor skill assessment and training.
  • The interdisciplinary nature of VR surgical simulation and ML research leads to varied reporting standards across computer science, medicine, and education.
  • Discrepancies in reporting hinder effective communication and knowledge transfer between these fields.

Purpose of the Study:

  • To develop a standardized checklist for reporting and analyzing studies on virtual reality surgical simulation and machine learning algorithms.
  • To provide a framework for assessing the quality and identifying deficiencies in manuscripts within this research area.
  • To enhance interdisciplinary communication and knowledge transfer.

Main Methods:

  • Development of the Machine Learning to Assess Surgical Expertise (MLASE) checklist.
  • Application of the MLASE checklist to 12 articles identified through a systematic literature review.
  • The articles focused on using machine learning to assess surgical expertise in virtual reality simulation.

Main Results:

  • Significant differences in reporting quality were observed between medical and computer science journals.
  • Medical journals excelled in discussion quality but were weaker in study design reporting.
  • Computer science journals showed the opposite trend, with stronger study design reporting and weaker discussion quality.

Conclusions:

  • The MLASE checklist serves as a valuable tool for authors and reviewers to ensure quality and consistency in reporting.
  • The checklist will help bridge the knowledge gap between computer science, medicine, and education.
  • Facilitates the growth of machine learning-assisted surgical education by improving research transparency and communication.