The possibility of using artificial intelligence in the diagnosis and treatment of urological diseases (review)

Bibliography:
1. Кок BL. Epidemiology of clinical benign prostatic hyperplasia. Asian J Urol 2017; 4 (3): 148-51.
2. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018; 319 (13): 1317-8.
3. Kanagasingam Y, Xiao D, Vignarajan J, et al. Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care. JAMA 2018; (1): e182665.
4. Drouin SJ, Yates DR, Hupertan V, et al. A systematic review of the tools available for predicting survival and managing patients with urothelial carcinomas of the bladder and of the upper tract in a curative setting. World J Urol 2013; (31): 10-6.
5. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg2018; (153): 77-1.
6. Snow PB, Smith DS, Catalona WJ. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J Urol 1994; 152: 192-6.
7. Babaian J, Fritsche H, Ayala A, et al. Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/mL. Urology 2000; (56): 1000-6.
8. Kwak JT, Hewitt SM. Nuclear architecture analysis of prostate cancer via convolutional neural networks. IEEE Access 2017; (5): 18526-33.
9. Nguyen TH, Sridharan S, Macias V, et al. Automatic Glea-son grading of prostate cancer using quantitative phase imaging and machine learning. J Biomed Opt 2017; (22): 36015.
10. Kim JK, Yook IH, Choi MJ, et al. A performance comparison on the machine learning classifiers in predictive pathology staging of prostate cancer. Stud Health Technol Inform 2017; (245): 1273.
11. Algohary A, Viswanath S, Shiradkar R, et al. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging 2018; (48): 818-28.
12. Wong NC, Lam C, Patterson L, Shayegan B. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 2019; (123): 51-7.
13. Hung AJ, Chen J, Che Z, et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J En-dourol 2018; (32): 438-44.
14. Hung AJ, Chen J, Ghodoussipour S, et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int 2019; 124 (3): 487-95.
15. Lam KM, He XJ, Choi KS. Using artificial neural network to predict mortality of radical cystectomy for bladder cancer. 2014 International Conference on Smart Computing (SMARTCOMP). Hong Kong, 2014; p. 201-7.
16. Wang G, Lam KM, Deng Z, Choi KS. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques. Comput Biol Med 2015; (63): 124-32.
17. Sapre N, Macintyre G, Clarkson M, et al. A urinary mi-croRNA signature can predict the presence of bladder urothelial carcinoma in patients undergoing surveillance. Br J Cancer 2016; (114): 454-62.
18. Bartsch JrG, Mitra AP, Mitra SA, et al. Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J Urol 2016; (195): 493-8.
19. Aminsharifi A, Irani D, Pooyesh S, et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol 2017; (31): 461-7.
20. Yang MY, Xia HZ, Zhu XH, et al. Application of machine learning models in predicting early stonefree rate after flexible ureteroscopic lithotripsy for renal stones. Beijing Da Xue Xue Bao Yi Xue Ban 2019; 51 (4): 653-9.
21. Seckiner I, Seckiner S, Sen H, et al. A neural network — based algorithm for predicting stone-free status after ESWL therapy. Int Braz J Urol 2017; (43): 1110-4.
22. Solovov VA. Neural network analysis in the diagnosis of prostate cancer. Vestnikof Samara University. Natural Science Se-rie 2005; 5 (39): 209-14.
23. Gantsev ShKh, Zimichev АА, Khrisanov NN, et al. Application of a neural network in predicting bladder cancer. Bashkortostan Medical Journal 2010; (3): 44-6.
24. Demchenko NA, Lukianov IV. Application of neural network modeling in patients undergoing radical prostatectomy. Cancer Urology 2011; (4): 67.
25. Popkov VM, Shatylko TV, Fomkin RN. Prediction of the result of pathohistological research of the prostate using an artificial neural network. Saratov Journal of Medical Scientific Research 2014; 10 (2): 328-32.
26. Shatylko TV, Popkov VM, Fomkin RN. Integral approach to preoperative determination of the clinical significance of prostate cancer. Saratov Journal of Medical Scientific Research 2015; 11 (3): 345-8.
27. Popkov VM, Shatylko TV, Korolev AYu, et al. PSA Screening Optimization with Artificial Intelligence. Bashkortostan Medical Journal 2015; 10 (3): 232-5.
28. Ershov AV, Kapsargin FP, Berezhnoi AG, et al. Expert systems in the evaluation of uroflogram data. Vestnik Urologii 2018; 6 (3): 12-6.
29. Kapsargin FP, Ershov AV, Zueva LF, et al. The use of neural networks in the choice of treatment for urolithiasis. Omskiy Nauchnyy Vestnik 2015; (1): 68-70.
30. Kotsar' AG, Seregin SP, Novikov AV. Automated urologist decision support system for the prediction and prevention of stone formation in urolithiasis. Urology 2013; 20 (2): 16-20.
31. Lukianov IV, Soshnikov DV Information and intellectual system for processing and recording data in the diagnosis and development of treatment tactics in patients with bladder outlet obstruction. In: Actual problems of urology: Materials of the III Congress of urologists of Kazakhstan. Almaty, 2000; 33-6.
32. Lukianov IV Symptoms of the lower urinary tract: prospects of diagnostic and therapeutic measures with the use of artificial intelligence elements. Consilium Medicum 2008; 2 (4): 24-6.