REVIEW   Free access

Minerva Urologica e Nefrologica 2020 February;72(1):49-57

DOI: 10.23736/S0393-2249.19.03613-0

Copyright © 2019 EDIZIONI MINERVA MEDICA

language: English

Artificial intelligence and neural networks in urology: current clinical applications

Enrico CHECCUCCI ¹ ✉, Riccardo AUTORINO ², Giovanni E. CACCIAMANI ³, Daniele AMPARORE ¹, Sabrina DE CILLIS ¹, Alberto PIANA ¹, Pietro PIAZZOLLA ⁴, Enrico VEZZETTI ⁴, Cristian FIORI ¹, Domenico VENEZIANO ⁵, Ash TEWARI ⁶, Prokar DASGUPTA ⁷, Andrew HUNG ³, Inderbir GILL ³, Francesco PORPIGLIA ¹ on behalf of the Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology 

¹ Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy; ² Division of Urology, VCU Health, Richmond, VA, USA; ³ USC Institute of Urology, University of Southern California, Los Angeles, CA, USA; ⁴ Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy; ⁵ Department of Urology and Renal Transplantation, Bianchi-Melacrino-Morelli Hospital, Reggio Calabria, Italy; ⁶ Icahn School of Medicine of Mount Sinai, New York, NY, USA; ⁷ King’s College London, Guy’s Hospital, London, UK

INTRODUCTION: As we enter the era of “big data,” an increasing amount of complex health-care data will become available. These data are often redundant, “noisy,” and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology.
EVIDENCE ACQUISITION: A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology.
EVIDENCE SYNTHESIS: The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology.
CONCLUSIONS: AI technologies are growing their role in health care; but, up to now, their “real-life” implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.

KEY WORDS: Artificial intelligence; Urology; Big data; Urologic neoplasms