Tumor growth models to generate pathologies for surgical training simulators

Area of Science:

• Medical Simulation
• Virtual Reality in Surgery
• Computational Pathology

Background:

• Minimally invasive procedures lack sufficient realistic training opportunities.
• Virtual reality (VR) offers riskless, condensed training for diverse surgical findings.
• High-fidelity simulators are needed for specialized procedures like hysteroscopy.

Purpose of the Study:

• To develop advanced tumor modeling techniques for virtual reality surgical training simulators.
• To create a high-fidelity hysteroscopy simulator capable of generating individualized surgical scenes.
• To accurately model the growth of common hysteroscopic pathologies, specifically uterine polyps and myomas.

Main Methods:

• Investigated various methods for generating tumor models suitable for VR surgical simulators.
• Developed and compared a cellular automaton model for tumor growth.
• Developed and compared a particle-based model for tumor growth.

Main Results:

• Successfully modeled the growth processes of uterine polyps and myomas.
• Generated macroscopically realistic findings for common hysteroscopic pathologies.
• Demonstrated the feasibility of creating individualized training scenarios through advanced modeling.

Conclusions:

• The presented cellular automaton and particle-based models enable the creation of realistic pathologies for hysteroscopy simulation.
• These models contribute to the development of high-fidelity VR surgical training systems.
• Individualized training scenarios with realistic pathologies can enhance surgical skill acquisition in hysteroscopy.