Checcucci, Autorino, Cacciamani, Amparore, De Cillis, Piana, Piazzolla, Vezzetti, Fiori, Veneziano, Tewari, Dasgupta, Hung, Gill, Porpiglia, (2020) Artificial intelligence and neural networks in urology: current clinical applications Minerva urologica e nefrologica = The Italian journal of urology and nephrology 72(1) 49-57

Abstract

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. 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. 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. 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.

Links

http://www.ncbi.nlm.nih.gov/pubmed/31833725
http://dx.doi.org/10.23736/S0393-2249.19.03613-0

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