Enrico Checcucci1, Riccardo Autorino2, Giovanni E Cacciamani3, Daniele Amparore4, Sabrina De Cillis4, Alberto Piana4, Pietro Piazzolla5, Enrico Vezzetti5, Cristian Fiori4, Domenico Veneziano6, Ash Tewari7, Prokar Dasgupta8, Andrew Hung3, Inderbir Gill3, Francesco Porpiglia4. 1. Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy - checcu.e@hotmail.it. 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 Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy. 5. Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy. 6. Department of Urology and Renal Transplantation, Bianchi-Melacrino-Morelli Hospital, Reggio Calabria, Italy. 7. Icahn School of Medicine of Mount Sinai, New York, NY, USA. 8. King's College London, Guy's Hospital, London, UK.
Abstract
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.
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.
Authors: Osamah Hasan; Alexandra Reed; Mohammed Shahait; Raju Chelluri; David I Lee; Ryan W Dobbs Journal: Int Urol Nephrol Date: 2022-07-29 Impact factor: 2.266
Authors: E Checcucci; D Amparore; G Volpi; F Piramide; S De Cillis; A Piana; P Alessio; P Verri; S Piscitello; B Carbonaro; J Meziere; D Zamengo; A Tsaturyan; G Cacciamani; Juan Gomez Rivas; S De Luca; M Manfredi; C Fiori; E Liatsikos; F Porpiglia Journal: World J Urol Date: 2021-09-01 Impact factor: 3.661
Authors: Malvika Sharma; Carl Savage; Monika Nair; Ingrid Larsson; Petra Svedberg; Jens M Nygren Journal: J Med Internet Res Date: 2022-10-05 Impact factor: 7.076
Authors: Sophie Knipper; Moritz Hagedorn; Maryam Sadat-Khonsari; Zhe Tian; Pierre I Karakiewicz; Derya Tilki; Hans Heinzer; Uwe Michl; Thomas Steuber; Franziska von Breunig; Christian Zöllner; Markus Graefen Journal: World J Urol Date: 2019-09-06 Impact factor: 4.226
Authors: B M Zeeshan Hameed; Aiswarya V L S Dhavileswarapu; Syed Zahid Raza; Hadis Karimi; Harneet Singh Khanuja; Dasharathraj K Shetty; Sufyan Ibrahim; Milap J Shah; Nithesh Naik; Rahul Paul; Bhavan Prasad Rai; Bhaskar K Somani Journal: J Clin Med Date: 2021-04-26 Impact factor: 4.241