Site Overlay

Future Perspectives: A Literature Review of the Urologist’s New Potential Tool-Artificial Intelligence

Volume 11, Issue 1

Original Article / Published: November 2023


K. Krastev, N. Tsvetkov, N. Stoyanov, K. Ibrahimov, K. Davidov

Clinic of Urology, UMBAL SofiaMed, Sofia


Introduction: The overview aims to present the current state of artificial intelligence (AI) tools in the decision-making process, diagnosis, treatment possibilities, or outcome predictions in functional urology.

Terminology: Artificial intelligence in healthcare includes computer systems that use reasoning and learning to analyze complex medical data and make predictions. Machine learning focuses on training computers with data to improve performance over time, especially in the case of processing medical information. Deep learning uses complex neural networks, inspired by the functional structure of the human brain, to extract information from medical data, improving the accuracy of diagnoses and speeding up investigations. Current AI Applications in Urology: The integration of artificial intelligence (AI) and artificial neural networks (ANN) in urological practice as a branch of medicine represents a promising potential for improving various aspects of patient care, diagnosis, surgical approaches with subsequent procedures resulting from rational and quick decision-making in the management of urological diseases (urolithiasis, BPH, oncology). AI technologies provide the opportunity to analyze large volumes of complex data, assist in clinical decision-making, optimize treatment strategies, and improve patient outcomes.

Conclusion: The future and potential prospects of artificial intelligence as an auxiliary tool are significant, and the complete transition to them is still ahead. The field of AI support in urology is rapidly evolving, with continuous research and development focused on increasing the accuracy of diagnostic tools, refining surgical techniques, and improving treatment optimization. Continued research and clinical trials will be crucial for validating the effectiveness of AI and directing their integration into everyday urological practice.


1. Beam A.L., Kohane I.S. Big Data and Machine Learning in Health Care. JAMA. 2018;319:1317–1318. doi: 10.1001/ jama.2017.18391

2. Diprose W., Buist N. Artificial intelligence in medicine: Hu- mans need not apply? New Zealand Med. J. 2016;129:73–76

3. Wang F, Casalino LP, Khullar D. Deep Learning in Medi- cine—Promise, Progress, and Challenges.JAMA Intern Med. 2019;179(3):293–294. doi:10.1001

4. Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithome-try: Combined Deep Learning and Thresholding Methods Yingpu Cui 1, Zhaonan Sun 1, Shuai Ma 1, Weipeng Liu 2, Xiangpeng Wang 2, Xiaodong Zhang 1, Xiaoying Wang 2021 Jun;23(3):436-445. doi: 10.1007/s11307-020-01554-0.

5. Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev. 2022 Jan;42(1):426-440. doi: 10.1002/med.21846. Epub 2021 Jul 26. PMID: 34309893.

6. Marconi L., Dabestani S., Lam T.B., Hofmann F., Stewart F., Nor- rie J., Bex A., Bensalah K., Canfield S.E., Hora M., et al. Systemat- ic Review and Meta-analysis of Diagnostic Accuracy of Percu- taneous Renal Tumour Biopsy. Eur. Urol. 2016;69:660–673. doi: 10.1016/j.eururo.2015.07.072.

7. Patel H., Druskin S.C., Rowe S.P., Pierorazio P.M., Gorin M.A., Allaf M.E. Surgical histopathology for suspected oncocytoma on renal mass biopsy: A systematic review and meta-analysis. BJU Int. 2017;119:661–666. doi: 10.1111/bju.13763.

8. Huang Y, Liu Z, He L, et al. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology. 2016;281(3):947-957.

9. Shkolyar E, Jia X, Chang TC, Trivedi D, Mach KE, Meng MQ, Xing L, Liao JC. Augmented Bladder Tumor Detection Us- ing Deep Learning. Eur Urol. 2019 Dec;76(6):714-718. doi: 10.1016/j.eururo.2019.08.032. Epub 2019 Sep 17. PMID: 31537407; PMCID: PMC6889816.

10. Smail LC, Dhindsa K, Braga LH, Becker S, Sonnadara RR. Using Deep Learning Algorithms to Grade Hydronephrosis Sever- ity: Toward a Clinical Adjunct. Front Pediatr. 2020 Jan 29;8:1. doi: 10.3389/fped.2020.00001. PMID: 32064241; PMCID: PMC7000524.

11. Parakh A, Lee H, Lee JH, Eisner BH, Sahani DV, Do S. Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization. Radiol Artif Intell. 2019 Jul 24;1(4):e180066. doi: 10.1148/ryai.2019180066. PMID: 33937795; PMCID: PMC8017404.

12. Patel VR, Tully AS, Holmes R, Lindsay J. Robotic radical prosta- tectomy in the community setting–the learning curve and beyond: initial 200 cases. J Urol. 2005;174(1):269-272.

13. Di Dio M, Barbuto S, Bisegna C, Bellin A, Boccia M, Amparore D, Verri P, Busacca G, Sica M, De Cillis S, Piramide F, Zaccone V, Piana A, Alba S, Volpi G, Fiori C, Porpiglia F, Checcucci E. Arti- ficial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications. Diag- nostics (Basel). 2023 Jul 10;13(14):2320. doi: 10.3390/diagnos- tics13142320. PMID: 37510065; PMCID: PMC10377834.

14. Federico Piramide, Karl-Friedrich Kowalewski, Giovanni Cacciamani, Ines Rivero Belenchon, Mark Taratkin, Umberto Carbonara, Michele Marchioni, Ruben De Groote, Sophie Knipper, Angela Pecoraro, Filippo Turri, Paolo Dell’Oglio, Stefa- no Puliatti, Daniele Amparore, Gabriele Volpi, Riccardo Campi, Alessandro Larcher, Alex Mottrie, Alberto Breda, Andrea Min- ervini, Ahmed Ghazi, Prokar Dasgupta, Ali Gozen, Riccardo Autorino, Cristian Fiori, Michele Di Dio, Juan Gomez Rivas, Francesco Porpiglia, Enrico Checcucci, Three-dimensional Model–assisted Minimally Invasive Partial Nephrectomy: A Systematic Review with Meta-analysis of Comparative Studies,European Urology Oncology,Volume 5, Issue 6,2022,Pages 640-650,ISSN 2588-9311

16. Artificial intelligence and simulation in urology [Article in En-glish, Spanish] J Gómez Rivas 1, C Toribio Vázquez 2, C Ballesteros Ruiz 2, M Taratkin 3, J L Marenco 4, G E Cacciamani 5, E

Checcucci 6, Z Okhunov 7, D Enikeev 8, F Esperto 9, R Grossmann 10, B Somani 11, D Veneziano 12 2021 Oct;45(8):524-529. doi: 10.1016/j.acuroe.2021.07.001.

16. Good DW, Stewart GD, Hammer S, et al. Using artificial intelligence and genomics to personalize treatment of advanced urothelial cancer. Nature. 2018;563(7732):344-349.

17. A systematic review on artificial intelligence in robot-assisted surgery Andrea Moglia 1, Konstantinos Georgiou, Evangelos Georgiou, Richard M Satava, Alfred Cuschieri 2021 Nov;95:106151. doi: 10.1016/j.ijsu.2021.106151.

18. Francesco Porpiglia, Enrico Checcucci, Daniele Amparore, Federico Piramide, Gabriele Volpi, Stefano Granato, Paolo Verri, Matteo Manfredi, Andrea Bellin, Pietro Piazzolla, Ric- cardo Autorino, Ivano Morra, Cristian Fiori, Alex Mottrie,Three-dimensional Augmented Reality Robot-assisted Partial Nephrectomy in Case of Complex Tumours (PADUA ≥10): A New Intraoperative Tool Overcoming the Ultrasound Guidance, European Urology, Volume 78, Issue 2,2020,Pages 229-238,ISSN 0302-2838,

19. Porpiglia, F., Checcucci, E., Amparore, D., Autorino, R., Piana, A.,Bellin, A., … & Fiori, C. (2018). Augmented-reality robot-assisted radical prostatectomy using hyper-accuracy three-dimen-sional reconstruction (ha3d™) technology: a radiological and pathological study. BJU International, 123(5), 834-845. https://

20. Artificial intelligence and neural networks in urology: current clinical applications Enrico Checcucci 1, Riccardo Autorino 2, Giovanni E Cacciamani 3, Daniele Amparore 4, Sabrina De Cillis 4, Alberto Piana 4, Pietro Piazzolla 5, Enrico Vezzetti 5, Cristian Fiori 4, Domenico Veneziano 6, Ash Tewari 7, Prokar Dasgupta 8, Andrew Hung 3, Inderbir Gill 3, Francesco Porpiglia 4; Uro-technology and SoMe Working Group of the Young Academic Urologists Working Party of the European Association of Urology 2020 Feb;72(1):49-57. doi: 10.23736/ S0393-2249.19.03613-0.

Volume 11, Issue 1


Artificial Intelligence, Artificial Neural Networks, Deep Learning. Machine Learning, Urology, Surgery.

How to cite this article:

K. Krastev, N. Tsvetkov, N. Stoyanov, K. Ibrahimov, K. Davidov. Future Perspectives: A Literature Review of the Urologist’s New Potential Tool – Artificial Intelligence. Journal of Endourology and Minimally Invasive Surgery (Bulgaria), 2023; 11(1): 5-10

Corresponding author:

Dr. Kristiyan Krastev
Clinic of Urology UMBAL “SofiaMed”, Sofia