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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-IbarrolaEmail author
  • Simon Hein
  • Gerd Reis
  • Christian Gratzke
  • Arkadiusz Miernik
Topic Paper
  • 100 Downloads

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 

Notes

Author contributions

Project development: RS and AM. Literature review and data extraction: RS. Manuscript drafting: RS, GR, and AM. Manuscript editing: SH, CG, and AM.

Funding

This research received no financial or other support.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Human and animal rights statement

This research did not involve human subjects or animals.

Ethical approval

As this is a review of the literature, no ethical approval was necessary.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Urology, Faculty of MedicineUniversity of Freiburg-Medical CentreFreiburgGermany
  2. 2.Department Augmented VisionGerman Research Center for Artificial IntelligenceKaiserslauternGermany

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