Cancer UrologyCancer UrologyОнкоурология1726-97761996-1812Publishing House ABV Press165710.17650/1726-9776-2023-19-2-148-152TOPICAL PROBLEMАКТУАЛЬНАЯ ТЕМАUse of artificial intelligence in diagnostic cystoscopy of bladder cancerИспользование искусственного интеллекта в цистоскопической диагностике рака мочевого пузыряhttps://orcid.org/0000-0001-5933-8346SadulaevaT. A.СадулаеваТ. А.
Russian Federation

Tanzila Adamovna Sadulaeva 

Build. 2, 8 Trubetskaya St., Moscow 119991, Russia 

 Танзила Адамовна Садулаева 

 Россия, 119991 Москва, ул. Трубецкая, 8, стр. 2 

tanzila.sadulaeva@mail.ruhttps://orcid.org/0000-0001-6067-8962EdilgireevaL. A.ЭдильгирееваЛ. А.
Russian Federation

Build. 2, 8 Trubetskaya St., Moscow 119991, Russia 

 Россия, 119991 Москва, ул. Трубецкая, 8, стр. 2 

snickers_192000@mail.ruhttps://orcid.org/0000-0002-3065-0755BimurzaevaM. B.БимурзаеваМ. Б.
Russian Federation

Build. 2, 8 Trubetskaya St., Moscow 119991, Russia 

 Россия, 119991 Москва, ул. Трубецкая, 8, стр. 2 

bimakka@mail.ruhttps://orcid.org/0000-0001-6694-837XMorozovA. O.МорозовА. О.
Russian Federation

Institute for Urology and reproductive health

Build. 2, 8 Trubetskaya St., Moscow 119991, Russia 

врач-уролог, кандидат медицинских наук, старший научный сотрудник Института урологии и репродуктивного здоровья человека

 Россия, 119991 Москва, ул. Трубецкая, 8, стр. 2 

andrei.o.morozov@gmail.comI.M. Sechenov First Moscow State Medical University, Ministry of Health of Russia (Sechenov University)ФГАОУ ВО Первый Московский государственный медицинский университет им. И.М. Сеченова Минздрава России (Сеченовский Университет)140820231921461522201202314042023https://oncourology.abvpress.ru/oncur/article/view/1657

Background. At the current stage of science and technology development, artificial intelligence (AI) is being actively developed and gradually introduced into the healthcare system.Aim. To perform a literature review to assess the diagnostic value of AI in the detection of bladder cancer at the cystoscopy stage.Materials and methods. We carried out a bibliographic search of articles in Medline and Embase databases using the keywords “artificial intelligence”, “cystoscopy”, “TURBT”.Results. Automated image processing based on AI can improve the accuracy of cancer diagnosis during cystoscopy. According to the studies presented in the review, the sensitivity of AI system for the detection of bladder cancer via cystoscopy can reach 89.7–95.4 %, while its specificity is 87.8–98.6 %, which exceeds the diagnostic capabilities of standard cystoscopy in white light, the sensitivity and specificity of which, according to recent investigations, are approximately 60 and 70 %, respectively. Despite the promising results of these studies, modern science is currently at the stage of developing and evaluating the performance of various AI methods used to analyze cystoscopy images. To date, it would be premature to introduce and widely use these technologies in healthcare, since there are no prospective clinical studies to assess the effectiveness of AI systems in diagnostic cystoscopy and transurethral resection of bladder cancer.Conclusion. Few studies show that AI-based cystoscopy is a promising approach to improvement of the quality of medical care for bladder cancer. Further research is needed to improve the diagnostic capabilities of AI and introduce the obtained technological data into clinical practice.

Введение. На современном этапе развития науки и техники происходят активная разработка и постепенное внедрение в систему здравоохранения технологий искусственного интеллекта (ИИ).Цель работы – обзор литературы для оценки диагностического значения ИИ в выявлении рака мочевого пузыря на этапе цистоскопии.Материалы и методы. Проведен библиографический поиск статей в базах данных Medline и Embase с использованием ключевых слов “artificial intelligence”, “cystoscopy”, “TURBT”.Результаты. Автоматизированная обработка изображений на основе ИИ может повысить точность диагностики рака при цистоскопии. По данным представленных исследований чувствительность цистоскопии при использовании ИИ достигает 89,7–95,4 %, специфичность – 87,8–98,6 %, что превосходит диагностические возможности стандартной цистоскопии в белом свете, чувствительность и специфичность которой составляют примерно 60 и 70 % соответственно. Несмотря на многообещающие результаты данных исследований, современная наука находится лишь на стадии разработки и оценки производительности различных методов ИИ, используемых для анализа цистоскопических изображений. На сегодняшний день рано говорить о внедрении и широком применении данных технологий в здравоохранении, так как отсутствуют проспективные клинические исследования оценки эффективности цистоскопической диагностики и трансуретральной резекции рака мочевого пузыря в сопровождении ИИ.Заключение. Цистоскопия на основе ИИ – перспективное направление (согласно немногочисленным данным литературы) в вопросе повышения качества медицинской помощи при раке мочевого пузыря. Для усовершенствования диагностических возможностей ИИ и внедрения в клиническую практику полученных технологических данных необходимо проведение дальнейших исследований.

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