Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

Journal: Journal of endourology
Published Date: Mar 20, 2018

Abstract

PURPOSE: Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP).

Authors

  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.
  • Jian Chen
    School of Pharmacy, Shanghai Jiaotong University, Shanghai, China.
  • Zhengping Che
    University of Southern California, Los Angeles, CA.
  • Tanachat Nilanon
    2 USC Machine Learning Center, Viterbi School of Engineering, University of Southern California , Los Angeles, California.
  • Anthony Jarc
    3 Medical Research, Intuitive Surgical, Inc. , Norcross, Georgia .
  • Micha Titus
    1 Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, USC Institute of Urology, University of Southern California , Los Angeles, California.
  • Paul J Oh
    1 Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, USC Institute of Urology, University of Southern California , Los Angeles, California.
  • Inderbir S Gill
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.

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