Complex health-care data will be growing exponentially as we enter the era of “big data”. Interpretation and analysis of a large amount of medical data for rapid and personalized decision-making require artificial intelligence-driven technologies and data mining techniques. Over the past decade, a rapid transition to the analysis, treatment and monitoring of kidney stone disease using the artificial intelligence (AI) with machine learning algorithms and artificial neural networks ensures the development of precise support systems. Machine learning algorithms able to predict the location of stones using computed tomography scans or ultrasound images, determine their chemical composition, recurrence rate based on demographic and genetic variables, spontaneous ureteral calculous passage, and treatment outcomes. Recent studies use rather simple machine learning methods and small training sets that reduce their accuracy, sensitivity, and specificity and limit their routine application. Therefore, the development of complex architectures, using multilayer artificial neural networks and convolutional neural networks, and integration other heterogeneous variables will improve the resultant predictive accuracy ensuring their introduction in real clinical practice.

BACKGROUND: Increasing the effectiveness of the treatment of patients with kidney cancer is one of the main problems of oncourology. In its solution, great importance is attached to the development of new surgical technologies. AIM: The aim of the study to evaluate the results of extracorporeal kidney resection in conditions of pharmaco-cold ischemia with orthotopic renal replantation in kidney cancer patients. Our study is aimed at assessing the results of extracorporeal resection of the kidney under pharmaco-cold ischemia with orthotopic replantation of renal vessels in patients with kidney cancer. MATERIALS AND METHODS: 44 patients [of them, 70.5% (n = 31) men and 29.5% (n = 13) women] with kidney cancer were recruited in a study. All patients were treated between 2012 and 2021. The mean age of patients was 55.92 12.6 years. The stage was determined using the TNM system: pT1a-3bN0M0-1 G1-3. 75% (n = 33) of patients had stage pT1a1b; 11.4% (n = 5) pT2a2b, one patient was present with multiple lesions; 13.6% (n = 6) pT3a3b, one patient had up to 15 lesions in a single kidney. Two previously operated patients had cancer of a single kidney with intraluminal invasion. The mean R.E.N.A.L nephrometric score was 10.32 1.34. RESULTS: The duration of the surgery was 402.07 83.21 minutes. The duration of cold ischemia was 149.9 53.1 minutes. Blood loss 751.1 633.6 ml. Renal vascular replacement was performed in 13 patients. Postoperative complications II degree according to Clavien Dindo were detected in 36.6% (16) of patients. There was only one lethal outcome due to mesenteric thrombosis at day 4. Disease progressed in 6.8% (n = 3) of cases. The GFR level before surgery was on average 72.3 16.8 ml / (min 1.73 m2), in the early postoperative period 58.7 28.3 ml / (min 1.73 m2), 1 year after surgery 69.4 26.2 ml / (min 1.73 m2). One year after surgery it was 69.4 26.2 mol/l. The follow-up period ranged from 8 to 86 months (on average 58.7 19.1 months). CONCLUSIONS: This technique is effective in patients with multiple foci, centrally located and large tumors, for hard-to-reach localizations, as well as in patients with the impossibility of intracorporeal pharmaco-cold ischemia, peculiarities of organ blood supply.