Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, ren
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Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer Rodrigo Suarez‑Ibarrola1 · Simon Hein1 · Gerd Reis2 · Christian Gratzke1 · Arkadiusz Miernik1 Received: 24 July 2019 / Accepted: 25 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Purpose The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date. Methods A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa). Results In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods. Conclusions The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine. Keywords Artificial intelligence · Machine learning · Deep learning · Artificial neural network · Convolutional neural network · Prostate cancer · Bladder cancer · Renal cell carcinoma · Urolithiasis
Introduction The term artificial intelligence (AI) commonly refers to the computational technologies that mimic or simulate intellectual processes typical of human cognitive function, such as reasoning, learning, and problem solving [1]. AI is a branch of computer science and part of a multidisciplinary approach * Rodrigo Suarez‑Ibarrola rodrigo.suarez@uniklinik‑freiburg.de 1
Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, 79106 Freiburg, Germany
Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
2
adopting principles from the fields of mathematics, logic, computation, and biology in an attempt to build intelligent entities often represented
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