The use of intelligent analysis (IA) in determining the tactics of treating patients with nephrolithiasis

Abstract

Introduction: The use of modern information technologies allows to increase confidence in the choice of a surgical treatment method of kidney stones, as well as to improve the quality of treatment due to an adequate combination of therapeutic techniques. Materials and methods: In our study we analyzed the treatment results of 625 patients with kidney stones. We created a register with the information on more than 50 parameters for each patient. Each example had an output parameter representing a predefined treatment strategy (extracorporeal shock-wave lithotripsy [ESWL]—1, percutaneous nephrolithotomy [PCNL]—2, pyelolithotomy or nephrolithotomy—3). The initial database served as the basis for training the neural network estimation technique. The aim of our study was to assess the possibility of using neural network algorithms in choosing a method for surgical treatment of urolithiasis. Results: A prospective study was conducted to assess the clinical effectiveness of implementing the recommendations of the system. The average number of sessions in the group using the neural network assessment technique was 1.4. Residual fragments remained at the time of discharge in seven (15.6%) patients: four in the kidney, three in the lower third of the ureter “stone path.” Inversion of therapeutic tactics—PCNL—was performed in four cases. The efficiency of the ESWL was 91.1%. The indicators of the ESWL in the comparison groups differed statistically significantly: in the second group, the efficiency was higher due to more stone fragmentation, with lower energy costs (the average number of sessions was 0.4 less). Conclusion: The presented technique can be of help for a practicing urologist to choose the optimal treatment method for each patient, thereby minimizing the risk of early postoperative complications.