Prospects of using computer vision technology to detect urinary stones and liver and kidney neoplasms on computed tomography images of the abdomen and retroperitoneal space

• DOI: 10.17816/dd515814
• Publication Date: 2024-03-11T10:21:33Z
• Authors: Yuriy A. Vasilev; Anton V. Vladzymy...; Kirill M. Arzamasov; David U. Shikhmur...; Andrey V. Pankratov; Iliya V. Ulyanov; Nikolay B. Nechaev
• Keywords: 03 medical and health sciences; 0302 clinical medicine

The article presents a selective literature review on the use of computer vision algorithms for the diagnosis of liver and kidney neoplasms and urinary stones using computed tomography images of the abdomen and retroperitoneal space. The review included articles published between January 1, 2020, and April 24, 2023. Pixel-based algorithms showed the greatest diagnostic accuracy parameters for segmenting the liver and its neoplasms (accuracy, 99.6%; Dice similarity coefficient, 0.99). Voxel-based algorithms were superior at classifying liver neoplasms (accuracy, 82.5%). Pixel- and voxel-based algorithms fared equally well in segmenting kidneys and their neoplasms, as well as classifying kidney tumors (accuracy, 99.3%; Dice similarity coefficient, 0.97). Computer vision algorithms can detect urinary stones measuring 3 mm or larger with a high degree of accuracy of up to 93.0%. Thus, existing computer vision algorithms not only effectively detect liver and kidney neoplasms and urinary stones but also accurately determine their quantitative and qualitative characteristics. Evaluating voxel data improves the accuracy of neoplasm type determination since the algorithm analyzes the neoplasm in three dimensions rather than only the plane of one slice.