Development and evaluation summaries of a percutaneous nephrolithotomy (PCNL) surgical simulator

Traditional surgical training methods, such as the use of cadavers, and porcine models (pigs) fall short of providing the realistic, real-time feedback necessary for mastering complex procedures. Virtual reality (VR) simulators with haptics offer a significant improvement, delivering immersive and interactive experiences that closely mimic real-life surgeries. Cadavers are also costly, averaging $1,300 each, and are in limited supply. Simulation devices for teaching surgical skills have been shown to significantly reduce surgical errors, enhancing both the safety and effectiveness of surgical training (Anderson & Abrahamson, 2017; Badash et al., 2016; Bushey, 2013; Chen et al., 2021; Chiang et al., 2013; Fried et al., 2005; Pottle, 2019; Sommer et al., 2021). While many existing surgical simulators incorporate elements of VR and haptics, they often lack full integration of a 3D VR operating environment that immerses surgeons completely in the procedural context. The Marion K181 PCNL Simulator addresses this gap by providing a comprehensive VR surgical simulation platform that integrates advanced haptic feedback and accurate 3D models to create a realistic training environment. This simulator aims to reduce the significant number of deaths caused by medical errors in the US, which total approximately 251,000 annually (James, 2013). These errors lead to additional surgeries, lower patient quality of life, and substantial costs to the healthcare system, estimated at $5.95 billion per year (Badash et al., 2016). The evaluation of the Marion K181 simulator demonstrates its effectiveness in improving surgical skills, with users reporting high levels of satisfaction with the realism of the haptic feedback and the accuracy of the anatomical models. This platform not only enhances the technical abilities of surgeons but also provides a cost-effective, ethical, and sustainable solution for surgical training.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Sainsbury, Ben

Advisors dc:contributor.advisor

• Green, Mark
• Ren, Jing

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1846

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1846