urovestВестник урологииUrology Herald2308-6424Rostov State Medical University10.21886/2308-6424-2023-11-3-142-148urovest-764Research ArticleОБЗОРЫ ЛИТЕРАТУРЫREVIEWS ARTICLEИспользование искусственного интеллекта в диагностике, лечении и наблюдении за пациентами с раком почкиUse of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancerhttps://orcid.org/0009-0002-0387-0768ТимофееваЕ. Ю.TimofeevaE. Yu.

Екатерина Юрьевна Тимофеева — студентка Института клинической медицины им. Н. В. Склифосовского

Москва

Ekaterina Yu. Timofeeva — Student, Sklifosovsky Institute for Clinical Medicine

Moscow

katetimofeeva_04@mail.ruhttps://orcid.org/0000-0002-7096-7423АзильгарееваК. Р.AzilgareevaС. R.

Камилла Руслановна Азильгареева — студентка Института клинической медицины им. Н. В. Склифосовского

Москва

Сamilla R. Azilgareeva — Student, Sklifosovsky Institute for Clinical Medicine

Moscow

camilla.azilgareeva@yandex.ruhttps://orcid.org/0000-0001-6694-837XМорозовА. О.MorozovA. O.

Андрей Олегович Морозов — канд. мед. наук; врач-уролог и старший научный сотрудник Института урологии и репродуктивного здоровья человека

Москва

Andrey O. Morozov — M.D., Cand.Sc.(Med); Urologist & Senior researcher, Institute for Urology and Reproductive Health

Moscow

andrei.o.morozov@gmail.comhttps://orcid.org/0000-0003-4369-173XТараткинМ. С.TaratkinM. S.

Марк Сергеевич Тараткин — врач-уролог; научный сотрудник Института урологии и репродуктивного здоровья человека

Москва

Mark S. Taratkin — M.D., Urologist & Researcher, Institute for Urology and Reproductive Health

Moscow

marktaratkin@gmail.comhttps://orcid.org/0000-0001-7169-2209ЕникеевД. В.EnikeevD. V.

Дмитрий Викторович Еникеев — д-р мед. наук, профессор; профессор Института урологии и репродуктивного здоровья человека; Доцент кафедры урологии; доцент

Москва

Вена, Австрия

Dmitry V. Enikeev — M.D., Cand.Sc.(Med), Prof.; Prof., Institute for Urology and Reproductive Health; Adjunct Prof., Dept. of Urology and Comprehensive Cancer Centre; Adjunct Prof.

Moscow

Vienna, Austria

dvenikeev@gmail.comПервый Московский государственный медицинский университет им. И. М. Сеченова (Сеченовский Университет)РоссияI.M. Sechenov First Moscow State Medical University (Sechenov University)Russian FederationПервый Московский государственный медицинский университет им. И. М. Сеченова (Сеченовский Университет); Венский медицинский университет; Университет здравоохранения им. Карла Ландштейнера — Институт урологии и андрологииРоссияI.M. Sechenov First Moscow State Medical University (Sechenov University); Medical University of Vienna; Karl Landsteiner University of Health Sciences — Institute of Urology and AndrologyRussian Federation202305102023113142148Copyright © Тимофеева Е.Ю., Азильгареева К.Р., Морозов А.О., Тараткин М.С., Еникеев Д.В., 20232023Тимофеева Е.Ю., Азильгареева К.Р., Морозов А.О., Тараткин М.С., Еникеев Д.В.Timofeeva E.Y., Azilgareeva С.R., Morozov A.O., Taratkin M.S., Enikeev D.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.urovest.ru/jour/article/view/764

Искусственный интеллект (ИИ) получил большое развитие за последнее десятилетие и стал предметом активных дискуссий. Данная тенденция обусловлена тем, что системы ИИ постоянно улучшаются за счёт усиления их вычислительных возможностей, а также получения больших объёмов данных. Благодаря этому ИИ может помочь в диагностике и выборе наиболее эффективного лечения. Цель работы — проанализировать возможности ИИ в диагностике, лечении и наблюдении за пациентами с почечно-клеточным раком (ПКР). ИИ демонстрирует большие перспективы в диагностике опухолей мочевыделительной системы, в возможности дифференцировать доброкачественные и злокачественные образования (благодаря системам машинного обучения), а также в прогнозировании гистологического подтипа опухоли. ИИ может использоваться на интраоперационном этапе (благодаря интеграции виртуальных 3D-моделей при оперативных вмешательствах), что позволяет снизить частоту тепловой ишемии и повреждения собирательной системы почки. ИИ находит своё применение при гистопатологической оценке: модель ИИ достигает 100% чувствительности и 97,1% специфичности в дифференциальной диагностике нормальной ткани от ПКР. Алгоритмы моделей ИИ могут быть использованы для выявления пациентов с высоким риском рецидива, требующих длительного наблюдения, а также для разработки индивидуальных стратегий лечения и наблюдения. Все вышеперечисленное доказывает возможность применения ИИ на всех этапах ведения пациентов с ПКР. Внедрение ИИ в медицинскую практику открывает новые перспективы для интерпретации и понимания сложных данных, недоступных для клиницистов.

Currently, artificial intelligence (AI) has developed greatly and has become the subject of active discussions. This is because artificial intelligence systems are constantly being improved by expanding their computing capabilities, as well as obtaining massive data. Due to this, AI can help to set a diagnosis and select the most effective treatment. The study aimed to analyse the possibilities of AI in the diagnosis, treatment and monitoring of patients with renal cell carcinoma (RCC). AI shows great prospects in the diagnosis urinary system lesions, in the ability to differentiate benign and malignant neoplasm (due to machine learning systems), as well as in predicting the histological subtype of the tumor. AI can be used at the intraoperative stage (thanks to the integration of virtual 3D models during surgical interventions), which reduces the frequency of thermal ischemia and damage to the kidney cavity system. AI finds its application in histopathological evaluation: the AI model reaches 100.0% sensitivity and 97.1% specificity in the differential diagnosis of normal tissue from RCC. AI model algorithms may be used to identify patients at high risk of relapse requiring long-term follow-up, as well as to develop individual treatment and follow-up strategies. All the above proves the possibility of using AI in all stages of the management of patients with RCC. The implementation of AI in medical practise opens new perspectives for the interpretation and understanding of complex data inaccessible to clinicians.

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The authors declare that there are no conflicts of interest present.