Artificial intelligence for planning patient management tactics after percutaneous nephrolithotripsy.
Authors:   N. Lopatkin Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiological Center, Shchamkhalova K.K. ,   Moscow, Russia, Merinov D.S. ,   the Department of Endourology of N. Lopatkin Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiologiсal Center; Moscow, Russia, Artemov A.V. ,   the operating unit with a sterilization room of N. Lopatkin Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiologiсal Center; Moscow, Russia, Gurbanov Sh.Sh. ,   the Department of Endourology of N. Lopatkin Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiological Center; Moscow, Russia, Apolikhin O.I. ,   N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Centre of Radiology of Ministry of health of Russian Federation; Moscow, Russia, Kaprin A.D. ,   the National Medical Research Centre of Radiology of Ministry of health of Russian Federation, director of P.A. Herzen Institution, Head of Department of Oncology and Radiology named after V.P. Kharchenko of RUDN University; Moscow, Russia
Published: November 2024

Introduction. Urolithiasis remains one of the most common urological pathologies that deserves due attention in the healthcare system. A current direction in optimizing percutaneous nephrolithotripsy (PСNL) is a personalized prognosis for patient management based on artificial intelligence (AI) decisionmaking algorithms. In our work, we applied an algorithm to create the most optimal tactics for patient management after PCNL. Purpose. Optimize the management of patients after PCNL based on AI. Materials and methods. The total number of patients treated at N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology andthose who took part in the study amounted to 1000 people. The number of men was 419 (41.9%), women 581 (58.1%). The age of patients included in the studies ranged from 18 to 88 years (52.3±13.47). The division into Training and Holdout partitions varied depending on the target variable to obtain the most accurate result. The maximum difference in the samples was 600n and 400n (60% and 40%), the minimum 800n and 200n (80% and 20%). Data processing was carried out using IBM SPSS Statistics and Modeler programs using the neural network modeling method. Results. The accuracy of prediction for the choice of nephroscope size was 82.2%, requirement for intraoperative stent placement 93.9%, requirement for ureteroscopy or contact ureterolithotripsy 98.5%. The accuracy of the forecast for the number of puncture accesses was 92.6%, for access through the upper group 95%, for access through the middle group 91.2%, for access through the lower group 91.2%. The algorithm allows us to predict the presence of residual stones with an accuracy of 84.1%, the duration of surgical intervention is 87.3% Conclusion. Further development of AI technology will not only provide a «second opinion» and reduce the time for processing large volumes of information, but will also standardize the approach to the treatment of complex clinical cases and help optimize the work of the urology department.