PenoMeter: a machine learning and algorithmic tool to advance Peyronie's disease assessment.

Journal: The journal of sexual medicine
Published Date:

Abstract

BACKGROUND: Peyronie's disease curvature assessment is a critical step for patient assessment; however, tools for objective, unbiased, and reproducible quantification are currently limited.

Authors

  • Reza Soltani
    Department of Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ali Balapour
    Bioinformatics Program, The University of British Columbia, Vancouver, V5T 4S6 BC, Canada.
  • Luke Witherspoon
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, The Ottawa Hospital, Ottawa, Ontario, Canada.
  • Abdullah Alhamam
    Department of Urologic Sciences, The University of British Columbia, Vancouver, V5Z 1M9 BC, Canada.
  • Ryan Flannigan
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, Weill Cornell Medicine, New York, New York. Electronic address: ryan.flannigan@ubc.ca.
  • Faraz Hach
    Vancouver Prostate Centre, M.H. Mohseni Institute of Urologic Sciences, Vancouver, V6H 3ZB BC, Canada.