Medical Radiology and Radiation Safety. 2025. Vol. 70. № 6
DOI:10.33266/1024-6177-2025-70-6-129-135
Yu.D. Udalov1, A.A. Sazhina1, A.A. Timchenko2, V.I. Pustovoit1
Modern Visualisaion Technologies and Artificial Intelligence in the Diagnosis of Kidney Lesions
1 A.I. Burnazyan Federal Medical Biophysical Center, Moscow, Russia
² Tinkoff Development Center, Moscow, Russia
Contact person: Anna Sazhina , e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Purpose: To summarize current approaches to the diagnosis of renal masses with a focus on the multimodal imaging and artificial intelligence (AI) technologies.
Material and methods: A structured literature review was conducted covering Russian and international sources published between 2015 and 2024. Diagnostic methods included ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), perfusion CT, and contrast-enhanced ultrasound (CEUS). Special attention was given to radiomics, machine learning algorithms (CNN, SVM, XGBoost), and the integration of augmented/virtual reality tools in oncological urology.
Results: CT and MRI demonstrate high diagnostic accuracy (sensitivity up to 95 %), while AI-based models such as KidneyNet and 3D RES-UNET achieve >96 % accuracy in tumor segmentation and classification. Radiomics and texture analysis support histological subtype differentiation, tumor grading prediction, and reduction of diagnostic subjectivity. AR/VR technologies enhance preoperative planning and personalized clinical decision-making.
Conclusions: The integration of AI technologies with multimodal imaging improves diagnostic precision, facilitates clinical workflows, and promotes the development of personalized oncological urology. Standardization of data acquisition protocols, external validation of algorithms, and resolution of ethical issues related to patient data use remain essential for large-scale implementation.
Keywords:radiation diagnostics, multimodal imaging, renal masses, artificial intelligence, radiomics, texture analysis, KidneyNet
For citation: Udalov YuD, Sazhina AA, Timchenko AA, Pustovoit VI. Modern Visualisaion Technologies and Artificial Intelligence in the Diagnosis of Kidney Lesions. Medical Radiology and Radiation Safety. 2025;70(6):129–135. (In Russian). DOI:10.33266/1024-6177-2025-70-6-129-135
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Conflict of interest. The authors declare no conflict of interest.
Financing. The study had no sponsorship.
Contribution. Article was prepared with equal participation of the authors.
Article received: 20.07.2025. Accepted for publication: 25.08.2025.