Scholarlink

Introduction <p>Benign prostatic hyperplasia (BPH) affects a growing proportion of the aging male population. Minimally invasive surgical therapies (MISTs) such as Rezum and UroLift offer effective alternatives to traditional approaches like transurethral resection of the prostate (TURP). However, training in these procedures is challenged by limited case exposure and variability across residency programs. Simulation-based training has emerged as a valuable tool to enhance surgical education. This study aims to assess the current evidence on simulation-based training for Rezum and UroLift, evaluating its validity, effectiveness, and potential integration with artificial intelligence (AI) in urology education.</p> Materials and methods <p>A systematic literature review was conducted on March 11, 2025, across PubMed, Scopus, Cochrane, and Google Scholar following PRISMA guidelines. Search terms included combinations of MIST techniques (Rezum, UroLift, iTIND) and training modalities (simulation, virtual reality, artificial intelligence). Studies were selected using PICOS criteria, focusing on urology trainees undergoing simulation-based training. Preclinical, review, and non-English studies were excluded.</p> Results <p>only 3 studies met the inclusion criteria: one focused on Ron between junior and senior residents, especially in implant placement and procedural technique. Simulation was highly rated by trainees in workshop settings, though predictive validity remains unproven.</p> Conclusion <p>Simulation-based training for Rezum and UroLift is a promising method to enhance resident competency in MIST procedures. Current evidence supports its face, content, and construct validity, though further studies are needed to confirm predictive validity and optimize training protocols. Integration of AI and telementoring may further improve training effectiveness and accessibility across institutions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Simulation-based training in minimally invasive surgical therapies (MIST): current evidence and future directions for artificial intelligence integration—a systematic review by EAU endourology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月18日

查看全文
  • Carlotta Nedbal,
  • Vineet Gauhar,
  • Thomas Herrmann,
  • Abhishek Singh,
  • Ali Talyshinskii,
  • Feras Al Jaafari,
  • Bhaskar Kumar Somani

摘要

Introduction

Benign prostatic hyperplasia (BPH) affects a growing proportion of the aging male population. Minimally invasive surgical therapies (MISTs) such as Rezum and UroLift offer effective alternatives to traditional approaches like transurethral resection of the prostate (TURP). However, training in these procedures is challenged by limited case exposure and variability across residency programs. Simulation-based training has emerged as a valuable tool to enhance surgical education. This study aims to assess the current evidence on simulation-based training for Rezum and UroLift, evaluating its validity, effectiveness, and potential integration with artificial intelligence (AI) in urology education.

Materials and methods

A systematic literature review was conducted on March 11, 2025, across PubMed, Scopus, Cochrane, and Google Scholar following PRISMA guidelines. Search terms included combinations of MIST techniques (Rezum, UroLift, iTIND) and training modalities (simulation, virtual reality, artificial intelligence). Studies were selected using PICOS criteria, focusing on urology trainees undergoing simulation-based training. Preclinical, review, and non-English studies were excluded.

Results

only 3 studies met the inclusion criteria: one focused on Ron between junior and senior residents, especially in implant placement and procedural technique. Simulation was highly rated by trainees in workshop settings, though predictive validity remains unproven.

Conclusion

Simulation-based training for Rezum and UroLift is a promising method to enhance resident competency in MIST procedures. Current evidence supports its face, content, and construct validity, though further studies are needed to confirm predictive validity and optimize training protocols. Integration of AI and telementoring may further improve training effectiveness and accessibility across institutions.