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
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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.
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).
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.
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.
KeywordsArtificial intelligence Machine learning Deep learning Artificial neural network Convolutional neural network Prostate cancer Bladder cancer Renal cell carcinoma Urolithiasis
Project development: RS and AM. Literature review and data extraction: RS. Manuscript drafting: RS, GR, and AM. Manuscript editing: SH, CG, and AM.
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.
As this is a review of the literature, no ethical approval was necessary.
- 1.Nuffield Council on Bioethics (2018) Bioethics briefing notes: artificial intelligence (AI) in healthcare and research. https://nuffieldbioethics.org/wp-content/uploads/Artificial-Intelligence-AI-in-healthcare-and-research.pdf. Accessed 21 Dec 2018
- 2.Frankish K, Ramsey WM (eds) (2014) Introduction. The Cambridge handbook of artificial intelligence. Cambridge University Press, Cambridge, pp 1–14Google Scholar
- 3.Stuart R, Norvig P (eds) (2010) Artificial intelligence—a modern approach, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
- 7.Curran Associates Inc. (2014) Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2014, December 8–13. Curran Associates Inc., vol 1Google Scholar
- 55.Chen J et al (2019) Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int [Epub ahead of print]Google Scholar