Physical patient simulators for surgical training: a review

Shao, Marine; Parraman, Carinna; Huson, David

Abstract

The purpose of this article is to review the fabrication process of physical patient simulators for surgical training and describes current research areas. Medical image acquisition and analysis are tools to reproduce human anatomy in 3D models. Data acquisition techniques include CT scans, MRI, and ultrasound. Postprocessing of this data is necessary to obtain a file for 3D printing. Two available fabrication methods are direct 3D printing of an organ model and 3D printing a mould to cast an organ replica. Direct 3D printing presents several limitations. Therefore, casting techniques with silicones and hydrogels are better suited for the fabrication of softer tissue models. Surgeons qualitatively evaluate the simulators and their ability to train students. It is also possible to make a quantitative evaluation to compare the properties of the simulators to the physical properties of organs. Different methods exist to measure the physical properties of soft tissues, mainly to find the Young modulus of the soft tissue. The tests can be in vivo, in situ or in vitro. Researchers perform tests on human tissues or animal tissues. The use of surgical simulators has shown satisfactory results in surgical training. Nonetheless, limitations remain, simulators lack realism and are not available for some pathologies. Future work in this area could be of benefit to
surgical training.

Citation

Shao, M., Parraman, C., & Huson, D. (2021). Physical patient simulators for surgical training: a review. In London Imaging Meeting (124-128). https://doi.org/10.2352/issn.2694-118X.2020.LIM-05

• Conference Name: London Imaging Meeting 2020: Future Colour Imaging
• Start Date: Sep 30, 2020
• End Date: Oct 1, 2020
• Acceptance Date: Jan 31, 2020
• Online Publication Date: Sep 29, 2021
• Publication Date: Sep 29, 2021
• Deposit Date: May 27, 2021
• Pages: 124-128
• Series ISSN: 2694-118X
• Book Title: London Imaging Meeting
• DOI: https://doi.org/10.2352/issn.2694-118X.2020.LIM-05
• Public URL: https://uwe-repository.worktribe.com/output/6241280
• Related Public URLs: https://www.imaging.org/site/IST/IST/Conferences/LIM/LIM_Home.aspx