Training models in laparoscopy: a systematic review comparing their effectiveness in learning surgical skills
W Willaert1, D Van De Putte, K Van Renterghem
1Department of Gastrointestinal Surgery, Ghent University Hospital, Ghent, Belgium. wouter.willaert@ugent.be
Acta Chirurgica Belgica
|June 8, 2013
Summary
Virtual reality (VR) and video trainer (VT) models effectively improve surgical skills compared to no training. Both VR and VT are equally effective, with VR outperforming traditional operating room training.
Area of Science:
- Medical Education
- Surgical Training Simulation
- Minimally Invasive Surgery
Background:
- Traditional surgical training in the operating room is time-consuming and can impact patient outcomes.
- Variable effectiveness of traditional methods necessitates exploration of alternative training models.
- Development of surgical training models aims to enhance skill acquisition and patient safety.
Purpose of the Study:
- To systematically review and compare the effectiveness of various laparoscopic surgical training models.
- To evaluate virtual reality (VR), video trainer (VT), robotic simulations, and cadaveric models against traditional training.
Main Methods:
- Systematic review of randomized clinical trials and cross-over studies.
- Searched databases: Pubmed, CENTRAL, and Science Citation Index Expanded.
- Included studies comparing training models, including comparisons to no training.
Main Results:
- Fifty-eight trials with 1591 participants were included; four had low risk of bias.
- Virtual reality (VR) and video trainer (VT) significantly improve surgical skills compared to no training.
- VR and VT demonstrate comparable effectiveness; VR training is superior to unstructured traditional operating room training.
Conclusions:
- Virtual reality (VR) and video trainer (VT) are validated and effective surgical teaching models.
- A combined approach of VR and VT is recommended for surgical curricula.
- Further research is needed to establish the comparative effectiveness of other training models.

