Open AccessReview
Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives
by
1,2,3,*, 4
, 2
, 5
, 6, 2
, 1,3, 7,8, 1,3,†
and 9,†
1
The BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy
2
Center of Research in Biomedical Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
3
Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
4
Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy
5
Department of Medical and Surgical Sciences University of Foggia, 71121 Foggia, Italy
6
LIDER-Lab, DIRPOLIS Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy
7
Department of Gastroenterology, Pomeranian Medical University, 71-252 Szczecin, Poland
8
The Centre for Digestive Diseases Endoklinika, 70-535 Szczecin, Poland
9
Department of Social Medicine & Public Health, Pomeranian Medical University, 71-252 Szczecin, Poland
*
Author to whom correspondence should be addressed.
†
These senior authors equally contributed to this work.
Academic Editor: Stephen P. Pereira
Received: 17 February 2021
/
Revised: 11 March 2021
/
Accepted: 16 March 2021
/
Published: 20 March 2021
Simple Summary
Over the last decades, visual endoscopy has become a gold standard for the detection and treatment of gastrointestinal cancers. However, mastering endoscopic procedures is complex and requires long hours of practice. In this context, simulation-based training represents a valuable opportunity for acquiring technical and cognitive skills, suiting the different trainees’ learning pace and limiting the risks for the patients. In this regard, the present contribution aims to present a critical and comprehensive review of the current technology for gastrointestinal (GI) endoscopy training, including both commercial products and platforms at a research stage. Not limited to it, the recent revolution played by the technological advancements in the fields of robotics, artificial intelligence, virtual/augmented reality, and computational tools on simulation-based learning is documented and discussed. Finally, considerations on the future trend of this application field are drawn, highlighting the impact of the most recent pandemic and the current demographic trends.