Artificial intelligence for a personalized approach to percutaneous nephrolithotripsy.

Abstract

Introduction. In recent years, there has been more and more research on the creation of artificial intelligence (AI) to help diagnose and treat patients in various fields of medicine. The use of algorithms allows us to reduce the time for processing large volumes of information and give a «second opinion» to a specialist in complex and non-standard clinical cases. In order to increase the effectiveness of percutaneous nephrolithotripsy, we have created an AI algorithm to personalize approach in the surgical treatment of nephrolithiasis. Purpose. To develop a universal tool for predicting the most optimal tactics of percutaneous nephrolithotripsy depending on the clinical case. Materials and methods. The total number of patients treated at N. Lopatkin Scientific Research Institute of Urology and Interventional Radiology and those 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%.The number of test observations was 30. The algorithm demonstrated high accuracy of prediction (from 73.8% to 94.5%) of surgical tactics in complex clinical cases. Conclusion. The results of using the algorithm for personalized prognosis in the surgical treatment of nephrolithiasis showed high accuracy and efficiency. Further development of technology to create algorithms for each nosology and surgical interventions will reduce the time of data analysis by the attending physician, provide a «second opinion» to young specialists and standardize the approach to treatment in rare and complex clinical cases.