Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist.

Journal: Urology
Published Date: Jan 1, 2021

Abstract

OBJECTIVE: To improve hypospadias classification system, we hereby, show the use of machine learning/image recognition to increase objectivity of hypospadias recognition and classification. Hypospadias anatomical variables such as meatal location, quality of urethral plate, glans size, and ventral curvature have been identified as predictors for postoperative outcomes but there is still significant subjectivity between evaluators.

Authors

  • Nicolás Fernández
    Division of Urology, Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, School of Medicine, Bogotá D.C., Colombia.
  • Armando J Lorenzo
    Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Mandy Rickard
    Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Michael Chua
    Department of Surgery, Division of Urology, Hospital for Sick Children, University of Toronto, Canada.
  • Joao L Pippi-Salle
    Division of Pediatric Urology, Sidra Medical and Research Center, Doha, Qatar.
  • Jaime Perez
    Division of Urology, Hospital Universitario San Ignacio, Pontificia Universidad Javeriana, Bogota, Colombia; Department of Urology, Fundación Santa Fe de Bogota. Bogota, Colombia.
  • Luis H Braga
    Clinical Urology Research Enterprise (CURE) Program, McMaster Children's Hospital, Hamilton, Ontario, Canada; McMaster Children's Hospital, McMaster University, Hamilton, Ontario, Canada; McMaster Pediatric Surgery Research Collaborative, McMaster University, Hamilton, Ontario, Canada.
  • Clyde Matava
    Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto 555 University Avenue, Toronto, ON, M5G 1X8, Canada. clyde.matava@sickkids.ca.

Keywords

External Resources

Popular Topics
Recent Journals