The possibilities of using artificial intelligence technologies to detect urinary tract concrements on computed tomography

Background. Urolithiasis (UCD) is a chronic disease associated with metabolic disorders in the body, which is accompanied by the formation of stones in the organs of the urinary system. Today, the problem of detecting stones in the urinary system, despite all the achievements, remains relevant (increase in morbidity, changes in living conditions (ecology, nutrition)). To solve the difficulties that arise in the process of diagnosing stones in the urinary system, as well as optimizing the diagnostic process itself, including the assessment of the information received and the formation of a protocol, it is currently proposed to use software based on artificial intelligence technologies (AI-based software).

Objective. To evaluate the diagnostic accuracy of AI services in the task of primary binary detection of radiopaque urinary system calculi (>100 HU) without stratification by morphometric characteristics in computed tomography (CT) studies.

Materials аnd methods. A data set of 100 CT scans of the abdominal organs and urinary system was taken from Unified Radiological Information Service of the Unified Medical Information and Analytical System (ERIS EMIAS) and sent for processing to two companies with software based on TII.

Results. Based on the results of testing on the prepared data set with signs of urinary tract stones, the software based on the TII achieved high indicators of diagnostic accuracy metrics exceeding the stated indicators: area under the curve (AUC) up to 0.998 (confidence interval (CI) 0.993–1.000) (AI-2 comprehensive service for analysis of abdominal organs (AOR)), specificity up to 1.000 ((CI 0.900–1.000), sensitivity up to 1.000 (CI 1.000–1.000).

Conclusion. High indicators of diagnostic accuracy of the software based on the TII based on the results of the analysis of the test data set showed that the TII algorithms can be used as a tool for optimizing the diagnostic process, including the detection and morphometry of urinary tract stones.

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