Development and Assessment of an AI-based Machine Learning Model for Predicting Urinary Continence and Erectile Function Recovery after Robotic-Assisted Radical Prostatectomy: Insights from a Prostate Cancer Referral Center.

Journal: Computer methods and programs in biomedicine
PMID: 39626503

Abstract

INTRODUCTION: Prostate cancer remains a significant health concern, with radical prostatectomy being a common treatment approach. However, predicting postoperative functional outcomes, particularly urinary continence and erectile function, poses challenges. Emerging artificial intelligence (AI) technologies offer promise in predictive modeling. This study aimed to develop and validate AI-based models to predict continence and potency following nerve-sparing robotic radical prostatectomy (RARP).

Authors

  • S Saikali
    AdventHealth Global Robotics Institute, FL, USA. Electronic address: shadysaikali@gmail.com.
  • S Reddy
    School of Medicine, Deakin University, Geelong, VIC, Australia.
  • M Gokaraju
    Andor® Health, Orlando, FL, USA.
  • N Goldsztein
    Andor® Health, Orlando, FL, USA.
  • A Dyer
    Andor® Health, Orlando, FL, USA.
  • A Gamal
    AdventHealth Global Robotics Institute, FL, USA.
  • A Jaber
    AdventHealth Global Robotics Institute, FL, USA.
  • M Moschovas
    AdventHealth Global Robotics Institute, FL, USA; University of Central Florida (UCF), FL, USA.
  • T Rogers
    AdventHealth Global Robotics Institute, FL, USA.
  • A Vangala
    Andor® Health, Orlando, FL, USA.
  • J Briscoe
    Andor® Health, Orlando, FL, USA.
  • C Toleti
    Andor® Health, Orlando, FL, USA.
  • P Patel
    Andor® Health, Orlando, FL, USA.
  • V Patel
    AdventHealth Global Robotics Institute, FL, USA; University of Central Florida (UCF), FL, USA.

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