AI Integration in Simulation-Based Training for Minimally Invasive BPH Surgery

Overview

This systematic review evaluates current evidence on artificial intelligence (AI) integration in simulation-based training for minimally invasive surgical therapies (MISTs) such as Rezum and UroLift. Findings highlight the potential of AI-enhanced simulators to improve skill acquisition, performance assessment, and training efficiency in urology residency programs.

Background

Benign prostatic hyperplasia (BPH) is increasingly prevalent in aging populations, prompting the development of minimally invasive surgical therapies like Rezum and UroLift as alternatives to traditional transurethral resection of the prostate (TURP). These MISTs offer advantages including better tolerability and ejaculation preservation but require specialized training due to their technical complexity. Simulation-based training provides a risk-free environment for residents to practice these procedures, with emerging AI technologies enhancing realism and assessment capabilities. Validating these simulators is essential to ensure effective skill transfer and improved patient outcomes.

Data Highlights

Key Findings

• Simulation-based training is critical for mastering Rezum and UroLift MIST procedures due to limited clinical exposure.
• AI-enhanced simulators provide realistic, interactive 3D environments facilitating skill acquisition and muscle memory development.
• The Rezum VR simulator demonstrated high reliability and could differentiate performance levels between senior and junior trainees.
• Performance metrics integrated by AI allow objective assessment of procedural skills, including treatment success and technical errors.
• Limited studies exist on simulation validity, especially construct validity, underscoring the need for further research.
• No simulation-based training studies were found for iTIND, indicating a gap in educational tools for this procedure.

Clinical Implications

Incorporating AI-powered simulation into urology training programs can enhance resident proficiency in minimally invasive BPH procedures, potentially reducing the learning curve and improving patient safety. Objective performance metrics enable tailored feedback and competency assessment, supporting more effective skill development. However, ongoing validation of these simulators is necessary to confirm their educational value and ensure transferability to clinical practice.

Conclusion

AI integration in simulation-based training for Rezum and UroLift shows promise in advancing urology education by improving training efficiency and skill assessment. Continued research and validation are essential to optimize these tools and expand their application across all minimally invasive BPH therapies.

References

1. Alsowayan et al. 2024 -- Evaluation of Rezum Simulator in Urology Training
2. EAU Endourology -- Systematic Review on AI and Simulation in MIST Training