medsovetМедицинский СоветMeditsinskiy sovet = Medical Council2079-701X2658-5790REMEDIUM GROUP Ltd.10.21518/ms2026-067medsovet-10105Research ArticleПРАКТИКАPRACTICEМетоды искусственного интеллекта, трехмерного и конечно-элементного моделирования в диагностике пролапса тазовых органов на основе визуализацииMethods of artificial intelligence, three-dimensional and finite element modeling in the diagnosis of pelvic organ prolapse based on visualizationhttps://orcid.org/0000-0001-7473-6692КаторкинС. Е.KatorkinS. E.

Каторкин Сергей Евгеньевич, д.м.н., профессор, заведующий кафедрой госпитальной хирургии

443089, Самара, ул. Чапаевская, д. 89

Sergei E. Katorkin, Dr. Sci. (Med.), Professor, Head of the Department and Clinic of Hospital Surgery

89, Chapaevskaya St., Samara, 443089

katorkinse@mail.ruhttps://orcid.org/0000-0002-9483-8909КолсановаА. В.KolsanovaA. V.

Колсанова Анна Владимировна, д.м.н., профессор, заведующая кафедрой акушерства и гинекологии Института педиатрии

443089, Самара, ул. Чапаевская, д. 89

Anna V. Kolsanova, Dr. Sci. (Med.), Professor, Head of the Department of Obstetrics and Gynecology, Institute of Pediatrics

89, Chapaevskaya St., Samara, 443089

a.v.kazakova@samsmu.ruhttps://orcid.org/0009-0003-7190-4795КаторкинаЕ. С.KatorkinaE. S.

Каторкина Елена Сергеевна, заведующая отделением гинекологии клиник 

443089, Самара, ул. Чапаевская, д. 89

Elena S. Katorkina, Head of the Department of Gynecology of Clinics 

89, Chapaevskaya St., Samara, 443089

e.s.katorkina@samsmu.ruСамарский государственный медицинский университетРоссияSamara State Medical UniversityRussian Federation202631052026205282302Copyright © Каторкин С.Е., Колсанова А.В., Каторкина Е.С., 20262026Каторкин С.Е., Колсанова А.В., Каторкина Е.С.Katorkin S.E., Kolsanova A.V., Katorkina E.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.med-sovet.pro/jour/article/view/10105Введение

Введение. Методы визуализации остаются основными в современной диагностике и планировании лечения пролапса органов таза (ПТО). Методы искусственного интеллекта (ИИ), трехмерного (3D) и конечно-элементного (КЭМ) моделирований становятся мощными инструментами с растущим признанием результатов.

Цель

Цель. Обобщить современные данные о применении технологий ИИ, 3D и КЭМ в диагностике и лечении ПТО.

Материалы и методы

Материалы и методы. Используя контрольный список PRISMA ScR, представленный в обзоре на основе области применения в качестве методологической основы, PubMed, Web of Science, Scopus и Cochrane Library были исследованы в период с января 2020 г. по декабрь 2025 г. В обзор включались исследования, применявшие алгоритмы ИИ к диагностическим методам визуализации (УЗИ, КТ, МРТ), а также 3D и КЭМ. Изучены современные данные для последующего определения мер, направленных на достижение наилучшей практики.

Результаты

Результаты. Извлечено 4 652 записи, отобрано 988 релевантных публикаций, 254 полнотекстовые статьи сохранены и проверены на основе заголовков и аннотаций. Затем 54 оценены на соответствие критериям включения и 32 статьи введены в исследование. Причины исключения 22 статей: нерелевантность для визуализации ПТО, недостаточная методологическая или диагностическая детализация, тип публикации. Исследования основывались на внутренних наборах данных с ограниченной интерпретируемостью моделей и отсутствием внешней валидации, поэтому клиническое внедрение и оценка результатов остаются недостаточно изученными.

Заключение

Заключение. Методы ИИ улучшают анализ изображений, оптимизируют рабочие процессы, обеспечивают индивидуальный подход и повышают эффективность диагностики и лечения ПТО. Технологии КЭМ результативны в функциональной компьютерной биомеханической оценке тазового дна. Персонализированное 3D-моделирование позволяет разработать оптимальную тактику хирургического лечения. В будущих исследованиях следует отдать приоритет внешней валидации, методологической строгости, стандартизации и внедрению в реальных условиях для преодоления разрыва между экспериментальными моделями и клинической полезностью.

Introduction

Introduction. Visualization methods remain fundamental in modern diagnosis and treatment planning for pelvic organ prolapse (POP). Artificial intelligence (AI), three-dimensional (3D), and finite element (FEM) modeling are emerging as powerful tools with growing recognition of their results.

Aim

Aim. To summarize current data on the use of AI, 3D, and FEM technologies in the diagnosis and treatment of POP. 

Materials and methods

Materials and methods. Using the PRISMA ScR checklist presented in the review, based on the scope of application, as a methodological framework, PubMed, Web of Science, Scopus, and the Cochrane Library were searched from January 2020 to December 2025. The review included studies applying AI algorithms to diagnostic imaging modalities (ultrasound, CT, MRI), as well as 3D and FEM. Current evidence was examined to identify measures aimed at achieving best practices.

Results

Results. 4,652 records were retrieved, 988 relevant publications were identified, and 254 full-text articles were retained and screened based on titles and abstracts. Fifty-four articles were then assessed for inclusion criteria, and 32 articles were included in the study. Reasons for excluding 22 articles included irrelevance for visualizing POP, insufficient methodological or diagnostic detail, and publication type. The studies were based on internal datasets with limited model interpretability and a lack of external validation, so clinical implementation and outcome assessment remain understudied.

Conclusions

Conclusions. AI methods improve image analysis, optimize workflows, provide a personalized approach, and increase the effectiveness of POP diagnosis and treatment. FEM technologies are effective in functional computer-aided biomechanical assessment of the pelvic floor. Personalized 3D modeling enables the development of optimal surgical treatment strategies. Future studies should prioritize external validation, methodological rigor, standardization, and implementation in real-world settings to bridge the gap between experimental models and clinical utility.

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