Annals of the Russian academy of medical sciencesAnnals of the Russian academy of medical sciencesВестник Российской академии медицинских наук0869-60472414-3545"Paediatrician" Publishers LLC1795310.15690/vramn17953INTERNAL DISEASES: CURRENT ISSUESАКТУАЛЬНЫЕ ВОПРОСЫ ВНУТРЕННИХ БОЛЕЗНЕЙResearch ArticleRadiomics in Urolithiasis: a Systematic Review of Current Applications, Limitations and Future DirectionsРадиомика при мочекаменной болезни: систематический обзор текущих применений, ограничений и будущих направленийhttps://orcid.org/0000-0002-6034-92691096-6331PranovichAlexandr A.ПрановичАлександр А.
Russian Federation

PhD in Biology, Senior Researcher

к.б.н., старший научный сотрудник

alex.pr76@mail.ruhttps://orcid.org/0000-0002-9357-09985964-2369KarmazanovskyGrigory G.КармазановскийГригорий Г.
Russian Federation

MD, PhD, Professor, Academician of the RAS

д.м.н., профессор, академик РАН

karmazanovsky@yandex.ruhttps://orcid.org/0000-0001-6419-01556315-7050SirotaEvgeniy S.СиротаЕвгений С.
Russian Federation

MD, PhD

д.м.н.

essirota@mail.ruhttps://orcid.org/0000-0002-0887-00816308-6260FirsovMikhail A.ФирсовМихаил А.
Russian Federation

PhD, Head of the Department of Urology

к.м.н., заведующий кафедрой урологии

Firsma@mail.ruhttps://orcid.org/0000-0002-9114-30524765-6498SimonovPavel A.СимоновПавел А.
Russian Federation

Assistant of the Department of Urology

ассистент кафедры урологии

doctorsimonov@mail.ruJunkerAlexander I.ЮнкерАлександр И.
Russian Federation

Applicant of the Department of Urology

соискатель кафедры урологии

junkeralex82@gmail.comhttps://orcid.org/0009-0003-1589-3817DzhatdoevaMariam K.ДжатдоеваМариям Х.
Russian Federation

Resident

ординатор

mari.dzh0709@mail.ruhttps://orcid.org/0009-0002-0487-8323KhubievDinislam A.ХубиевДинислам А.
Russian Federation

MD, Head of the Urology Department

заведующий урологическим отделением

Dinislamx@mail.ruA.V. Vishnevsky National Medical Research Center of SurgeryНациональный медицинский исследовательский центр хирургии имени А.В. ВишневскогоI.M. Sechenov First Moscow State Medical University (Sechenov University)Первый Московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет)V.F. Voyno-Yasenetsky Krasnoyarsk State Medical UniversityКрасноярский государственный медицинский университет им. проф. В.Ф. Войно-ЯсенецкогоMedical Care Center “DocCity”Центр медицинской помощи «DocCity»28122024131220247953934052102202426072024Copyright ©; 2024, "Paediatrician" Publishers LLCCopyright ©; 2024, Издательство "Педиатръ"2024"Paediatrician" Publishers LLCИздательство "Педиатръ"https://vestnikramn.spr-journal.ru/jour/about/submissionshttps://vestnikramn.spr-journal.ru/jour/article/view/17953

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.

Приведенный анализ доступных в международной литературе исследований показывает, что применение радиомики при мочекаменной болезни представляет собой быстро развивающееся направление в медицинской науке. Судя по общему числу исследований, включенных в настоящий обзор, очевидно, что диагностические приложения, которые связаны с лучевой диагностикой, в основном близки к внедрению в урологическую практику, в то время как во многих работах авторы утверждали, что функция предложенной ими модели может быть дополнительно оптимизирована после введения большего количества данных. Текстурный анализ изображений камней значительно повысил точность прогнозирования типа камня в почках. Такие достижения в области технологий медицинской визуализации и машинного обучения, вероятно, будут более широко использоваться в рутинном клиническом лечении мочекаменной болезни в ближайшем будущем. Тем не менее еще есть возможности для дальнейшего совершенствования алгоритмов машинного обучения в целях повышения чувствительности и специфичности методов автоматической классификации.

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