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

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Abstract

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.

About the authors

Yuriy A. Vasilev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; National Medical and Surgical Center Named after N.I. Pirogov

Email: npcmr@zdrav.mos.ru
ORCID iD: 0000-0002-0208-5218
SPIN-code: 4458-5608

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow; Moscow

Anton V. Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies; I.M. Sechenov First Moscow State Medical University

Email: VladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Moscow; Moscow

Kirill M. Arzamasov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: ArzamasovKM@zdrav.mos.ru
ORCID iD: 0000-0001-7786-0349
SPIN-code: 3160-8062

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

David U. Shikhmuradov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: ShikhmuradovDU@zdrav.mos.ru
ORCID iD: 0000-0003-1597-5786
SPIN-code: 9641-0913

MD

Russian Federation, Moscow

Andrey V. Pankratov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: PankratovAV3@zdrav.mos.ru
ORCID iD: 0009-0008-4741-4530

MD

Russian Federation, Moscow

Iliya V. Ulyanov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: UlyanovIV2@zdrav.mos.ru
ORCID iD: 0000-0002-8330-6069
SPIN-code: 5898-3242

MD

Russian Federation, Moscow

Nikolay B. Nechaev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: NechaevNB@zdrav.mos.ru
ORCID iD: 0009-0007-9219-7726
SPIN-code: 3232-1545

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. An example of liver neoplasm segmentation using one of the algorithms.

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3. Fig. 2. An example of liver neoplasm segmentation by an algorithm based on a contrast-enhanced CT scan.

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4. Fig. 3. An example of right kidney neoplasm segmentation.

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5. Fig. 4. An example of urinary stone detection using one of the algorithms.

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