Radiomics in Urolithiasis: a Systematic Review of Current Applications, Limitations and Future Directions

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Abstract

Radiomic image analysis of kidney stones has significantly improved the accuracy of kidney stone type prediction. Such advances in medical imaging technologies and machine learning are likely to be more widely used in routine clinical management of KSD in the near future. However, there is still room for further improvement of machine learning algorithms to improve the sensitivity and specificity of automatic classification methods. Creating a network of centralized database for each type of stone, including demographic and general information about patients, urine, blood parameters, other laboratory tests, treatment methods and their results, will allow the development of a more reliable machine learning algorithm for personalized medicine for KSD.

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About the authors

Alexandr A. Pranovich

A.V. Vishnevsky National Medical Research Center of Surgery

Author for correspondence.
Email: alex.pr76@mail.ru
ORCID iD: 0000-0002-6034-9269
SPIN-code: 1096-6331

PhD in Biology, Senior Researcher

Россия, Moscow

Grigory G. Karmazanovsky

A.V. Vishnevsky National Medical Research Center of Surgery

Email: karmazanovsky@yandex.ru
ORCID iD: 0000-0002-9357-0998
SPIN-code: 5964-2369

MD, PhD, Professor, Academician of the RAS

Россия, Moscow

Evgeniy S. Sirota

I.M. Sechenov First Moscow State Medical University (Sechenov University)

Email: essirota@mail.ru
ORCID iD: 0000-0001-6419-0155
SPIN-code: 6315-7050

MD, PhD

Россия, Moscow

Mikhail A. Firsov

V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University

Email: Firsma@mail.ru
ORCID iD: 0000-0002-0887-0081
SPIN-code: 6308-6260

PhD, Head of the Department of Urology

Россия, Krasnoyarsk

Pavel A. Simonov

V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University

Email: doctorsimonov@mail.ru
ORCID iD: 0000-0001-6741-5428
SPIN-code: 2339-2848

Assistant of the Department of Urology

Россия, Krasnoyarsk

Alexander I. Junker

V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University

Email: junkeralex82@gmail.com

Applicant of the Department of Urology

Россия, Krasnoyarsk

Mariam K. Dzhatdoeva

A.V. Vishnevsky National Medical Research Center of Surgery

Email: mari.dzh0709@mail.ru
ORCID iD: 0009-0003-1589-3817

Resident

Россия, Moscow

Dinislam A. Khubiev

Medical Care Center “DocCity”

Email: Dinislamx@mail.ru
ORCID iD: 0009-0002-0487-8323

MD, Head of the Urology Department

Россия, Cherkessk

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