Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects.

Journal: World journal of urology
Published Date: Jan 10, 2020

Abstract

BACKGROUND: Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.

Authors

  • Misgana Negassi
    Department of Sustainable Systems Engineering INATECH, University of Freiburg, Emmy-Noether-Straße 2, Freiburg, Germany.
  • Rodrigo Suarez-Ibarrola
    Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany. rodrigo.suarez@uniklinik-freiburg.de.
  • Simon Hein
    Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106, Freiburg, Germany.
  • Arkadiusz Miernik
    Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany.
  • Alexander Reiterer
    Department of Sustainable Systems Engineering INATECH, University of Freiburg, Emmy-Noether-Straße 2, Freiburg, Germany. alexander.reiterer@ipm.fraunhofer.de.

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