Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

Journal: Urology practice
Published Date: Jun 22, 2023

Abstract

INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise.

Authors

  • Nathan Schuler
    Simulation Innovation Laboratory, Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Lauren Shepard
    Simulation Innovation Lab, Carnegie Center for Surgical Innovation, Johns Hopkins University, Baltimore, Maryland.
  • Aaron Saxton
    Department of Urology, University of Rochester Medical Center, Rochester, New York.
  • Jillian Russo
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Daniel Johnston
    University of Rochester School of Medicine and Dentistry, Rochester, New York.
  • Patrick Saba
    Simulation Innovation Laboratory, Department of Urology, Transplant, University of Rochester Medical Center, Rochester, New York, USA.
  • Tyler Holler
    Simulation Innovation Laboratory, Department of Urology, University of Rochester Medical Center, Rochester, New York, USA.
  • Andrea Smith
    Data and Analytics, Intuitive Surgical, Inc, Peachtree Corners, Georgia.
  • Sue Kulason
    Data and Analytics, Intuitive Surgical, Inc, Peachtree Corners, Georgia.
  • Andrew Yee
    Data and Analytics, Intuitive Surgical, Inc., Peachtree Corners, GA, 30092, USA.
  • Ahmed Ghazi
    Department of Urology, University of Rochester Medical Center, Rochester, NY, USA.

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