Методы искусственного интеллекта, трехмерного и конечно-элементного моделирования в диагностике пролапса тазовых органов на основе визуализации

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

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. 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. 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.

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