vmireavizВестник медицинского института «РЕАВИЗ». Реабилитация, Врач и ЗдоровьеBulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH)2226-762X2782-1579РЕАВИЗ10.20340/vmi-rvz.2025.1.MORPH.1vmireaviz-1164Research ArticleМОРФОЛОГИЯ, ПАТОЛОГИЯMORPHOLOGY, PATHOLOGYВозможности использования технологий искусственного интеллекта в морфологической диагностике воспалительных заболеваний кишечника (обзор литературы)Possibilities of using artificial intelligence technologies in the morphological diagnosis of inflammatory bowel diseases (literature review)https://orcid.org/0009-0007-1957-9074ЧуриловаЕ. Г.ChurilovaE. G.

Чурилова Елизавета Геннадьевна – cтудентка.

ул. Трубецкая, д. 8/2, Москва, 119991

Elizaveta G. Churilova - Student, First Moscow State Medical University named after I.M. Sechenov (Sechenov University).

8/2, Trubetskaya st., Moscow, 119991

churilova_ee@mail.ruhttps://orcid.org/0009-0003-6481-6017КазумоваА. Б.KazumovaA. B.

Казумова Аглая Борисовна – студентка.

ул. Трубецкая, д. 8/2, Москва, 119991

Aglaya B. Kazumova - Student, First Moscow State Medical University named after I.M. Sechenov (Sechenov University).

8/2, Trubetskaya st., Moscow, 119991

marlattmargaret@gmail.comhttps://orcid.org/0000-0003-4683-1953АхриеваХ. М.AkhrievaKh. M.

Ахриева Хава Мусаевна - канд. мед. наук, заведующая кафедрой факультетской терапии медицинского факультета.

д. 7, Магас, Республика Ингушетия, 386001

Khava M. Akhrieva - Cand. Sci. (Med.), Head of the Department of Faculty Therapy, Faculty of Medicine, Ingush State University.

7, I.B. Zyazikov Avenue, Magas, Republic of Ingushetia, 386001

akhrievakhava@mail.ruhttps://orcid.org/0000-0002-8136-0117ПачуашвилиН. В.PachuasviliN. V.

Пачуашвили Нано Владимеровна - канд. мед. наук, научный сотрудник лаборатории эндокринной биофотоники.

ул. Трубецкая, д. 8/2, Москва, 119991; ул. Дмитрия Ульянова, д. 11, Москва, 117292

Nano V. Pachuashvili - Cand. Sci. (Med.), Researcher, Laboratory of Endocrine Biophotonics, National Medical Research Center of Endocrinology.

8/2, Trubetskaya st., Moscow, 119991; 11, Dmitry Ulyanov st., Moscow, 117292

npachuashvili@bk.ruhttps://orcid.org/0000-0001-5635-6100ТертычныйА. С.TertychnyyA. S.

Тертычный Александр Семенович - д-р мед. наук, профессор, заведующий лабораторией электронной микроскопии и иммуногистохимии Института клинической морфологии и цифровой патологии.

ул. Трубецкая, д. 8/2, Москва, 119991

Aleksandr S. Tertychnyy - Dr. Sci. (Med.), Professor, Head of the Laboratory of Electron Microscopy and Immunohistochemistry, Institute of Clinical Morphology and Digital Pathology, I.M. Sechenov First Moscow State Medical University (Sechenov University).

8/2, Trubetskaya st., Moscow, 119991

atertychnyy@yandex.ruПервый московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет)РоссияSechenov First Moscow State Medical University (Sechenov University)Russian FederationИнгушский государственный университетРоссияIngush State UniversityRussian FederationПервый московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет); Национальный медицинский исследовательский центр эндокринологииРоссияSechenov First Moscow State Medical University (Sechenov University); National Medical Research Center of EndocrinologyRussian Federation2025100320251512229Copyright © Чурилова Е.Г., Казумова А.Б., Ахриева Х.М., Пачуашвили Н.В., Тертычный А.С., 20252025Чурилова Е.Г., Казумова А.Б., Ахриева Х.М., Пачуашвили Н.В., Тертычный А.С.Churilova E.G., Kazumova A.B., Akhrieva K.M., Pachuasvili N.V., Tertychnyy A.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.reaviz.ru/jour/article/view/1164 

 

Введение

Введение. Воспалительные заболевания кишечника (ВЗК), включая болезнь Крона (БК) и язвенный колит (ЯК), представляют собой хронические патологии желудочно-кишечного тракта иммуновоспалительного генеза. Основное внимание в работе уделено роли искуственного интеллекта (ИИ) в морфологической диагностике ВЗК, эндоскопической визуализации, прогнозировании исходов и мониторинге пациентов. Цель: обобщить данные о применении методов ИИ в диагностике и лечении ВЗК, включая анализ цифровых изображений, прогнозирование ремиссии и активности воспалительного процесса, а также автоматизацию процессов гистологической и эндоскопической оценки. Материалы и методы. Проанализированы современные исследования, посвящённые применению технологий машинного обучения (ML) и глубокого обучения (DL) в диагностике ВЗК. Особое внимание уделено методам обработки гистологических изображений, нейросетевым алгоритмам для классификации стадий воспаления, а также использованию ИИ для эндоскопической визуализации в режиме реального времени. Результаты. Технологии ИИ обеспечивают более точное и объективное определение гистологической активности воспаления, используя индексы Гебоэса, Нэнси и Робартса. Применение глубоких нейронных сетей (CNN) позволяет автоматически классифицировать стадии воспалительного процесса и выявлять остаточное воспаление, что критично для предотвращения рецидивов и риска развития колоректального рака. Использование эндоцитоскопии и алгоритмов визуализации в реальном времени повышает точность раннего выявления дисплазии слизистой оболочки. Нейронные сети и другие ML-алгоритмы демонстрируют высокую чувствительность и специфичность в разграничении БК и ЯК, а также в оценке гистологической ремиссии. Заключение. ИИ становится неотъемлемой частью диагностики ВЗК, улучшая точность морфологических исследований, оптимизируя эндоскопические методы и снижая вероятность ошибок. Интеграция ИИ в клиническую практику позволяет расширить возможности лечения, включая персонализированные подходы и долгосрочный мониторинг пациентов.

Introduction. Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-inflammatory pathologies of the gastrointestinal tract. This study focuses on the role of artificial intelligence (AI) in the morphological diagnosis of IBD, endoscopic visualization, outcome prediction, and patient monitoring. Aim: To summarize data on the application of AI methods in the diagnosis and treatment of IBD, including digital image analysis, remission prediction, inflammation activity evaluation, and the automation of histological and endoscopic assessment processes. Materials and methods. Modern studies on the use of machine learning (ML) and deep learning (DL) technologies in the diagnosis of IBD were analyzed. Special attention was paid to histological image processing methods, neural network algorithms for inflammation staging, and the use of AI for real-time endoscopic visualization. Results. AI technologies provide more accurate and objective determination of histological inflammation activity using Geboes, Nancy, and Robarts indices. Deep neural networks (CNN) enable automatic classification of inflammation stages and the detection of residual inflammation, which is critical for preventing relapses and reducing colorectal cancer risk. Endocytoscopy and real-time visualization algorithms improve the accuracy of early detection of mucosal dysplasia. Neural networks and other ML algorithms demonstrate high sensitivity and specificity in distinguishing CD from UC and assessing histological remission. Conclusion. AI is becoming an integral part of IBD diagnostics, enhancing the accuracy of morphological studies, optimizing endoscopic methods, and reducing error rates. Integrating AI into clinical practice expands treatment possibilities, including personalized approaches and long-term patient monitoring.

Болезнь Крона [MeSH ID: D003424]Язвенный колит [MeSH ID: D003093]Воспалительные заболевания кишечника [MeSH ID: D015212]Искусственный интеллект [MeSH ID: D001185]Машинное обучение [MeSH ID: D065007]Глубокое обучение [MeSH ID: D000082062]Гисто-патология [MeSH ID: D006660]Эндоскопия [MeSH ID: D004724]Колоректальный рак [MeSH ID: D015179]Нейронные сети искусственные [MeSH ID: D017209]Crohn Disease [MeSH ID: D003424]ColitisUlcerative [MeSH ID: D003093]Inflammatory Bowel Diseases [MeSH ID: D015212]Artificial Intelligence [MeSH ID: D001185]Machine Learning [MeSH ID: D065007]Deep Learning [MeSH ID: D000082062]Histopathology [MeSH ID: D006660]Endoscopy [MeSH ID: D004724]Colorectal Neoplasms [MeSH ID: D015179]Neural NetworksComputer [MeSH ID: D017209]ReferencesУспенский Ю.П., Иванов С.В., Фоминых Ю.А., Наркевич А.Н., Сегаль А.М., Гржибовский А.М. 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The authors declare that there are no conflicts of interest present.