Application of Artificial Intelligence Technologies and Software for Measuring the Stones’ Volume According to Computed Tomography in Patients with Urolithiasis (Literature Review)

Aim. The choice of surgical treatment method and the prognosis of its effectiveness in urolithiasis depends on the MSCT characteristics of the stone: localization, size, density. In the literature, a CT parameter of a concretion, its volume, is proposed as a promising predictor of the success of surgical intervention. This study examines approaches to volumetry of urinary stones using software and artificial intelligence.

Materials and Methods. The most relevant and cited studies, fundamental work on the automated determination of the volume of concretion, current clinical recommendations for the diagnosis and treatment of ICD, posted in scientifi databases in the public domain, have been studied.

Results. Modern approaches to automated volumetry of kidney and ureter calculi are analyzed, and the practical significance of the results of measuring lithological volume by software and artificial intelligence algorithms is demonstrated.

Conclusion. Automated methods of volumetry of urinary stones according to computed tomography exceed the accuracy of the results of the calculation of the lithological volume by a radiologist. The use of software and artificial intelligence methods can improve the diagnostic accuracy and reproducibility of measurements in patients with urolithiasis, and optimize the work of radiation diagnostics departments.

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