A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.

Abstract

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.
• Saum Ghodoussipour
  Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
• 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.
• Zequn Liu
  School of Electronics Engineering and Computer Science, Peking University, Beijing, China.
• Jessica Nguyen
  Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
• Sanjay Purushotham
  University of Southern California, Los Angeles, CA, USA.
• 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.

Keywords

External Resources