cardiotomskСибирский журнал клинической и экспериментальной медициныSiberian Journal of Clinical and Experimental Medicine2713-29272713-265XTSU publishing10.29001/2073-8552-2025-40-2-211-217cardiotomsk-2746Research ArticleЦИФРОВЫЕ ТЕХНОЛОГИИ В МЕДИЦИНЕ И ЗДРАВООХРАНЕНИИDIGITAL TECHNOLOGIES IN MEDICINE AND HEALTHCAREПрименение искусственного интеллекта в патологической анатомииApplication of artificial intelligence in pathologyhttps://orcid.org/0000-0001-6578-0050ШворобД. С.ShvorobD. S.

Швороб Данил Сергеевич, ассистент, кафедра патологической анатомии

 

283001, Донецкая Народная Республика, Донецк, ул. Артема, 58

Danil S. Shvorob, Assistant Professor, Pathological Anatomy Department

16, Il’icha ave., Donetsk, 283003, Donetsk People's Republic

mcshady@mail.ruhttps://orcid.org/0000-0001-9362-2279ВасяеваТ. А.VasyaevaT. A.

Васяева Татьяна Александровна, канд. техн. наук, доцент, декан факультета информационных систем и технологий

283003, Донецкая Народная Республика, Донецк, пр. Ильича, 16

Tatyana A. Vasyaeva, Ph.D. in Engineering, Associate Professor, Dean of the Faculty

58, Artema str., Donetsk, 283001, Donetsk People's Republic

vasyaeva@gmail.comhttps://orcid.org/0009-0001-2070-2371ХрюкинЕ. А.KhriukinE. A.

Хрюкин Евгений Александрович, аспирант, кафедра автоматизированных систем управления

283003, Донецкая Народная Республика, Донецк, пр. Ильича, 16

Evgeniy A. Khriukin, Graduate Student, Automated Control Systems Department 

58, Artema str., Donetsk, 283001, Donetsk People's Republic

wadealer@yandex.ruhttps://orcid.org/0009-0004-6166-0288ПапакинаА. В.PapakinaA. V.

Папакина Анна Владимировна, студент 6-го курса

283001, Донецкая Народная Республика, Донецк, ул. Артема, 58

Anna V. Papakina, 6th-year student

16, Il’icha ave., Donetsk, 283003, Donetsk People's Republic

annapapakina6@gmail.comДонецкий государственный медицинский университет имени М. ГорькогоРоссияM. Gorky Donetsk State Medical UniversityRussian FederationДонецкий национальный технический университетРоссияDonetsk National Technical UniversityRussian Federation202513072025402211217Copyright © Швороб Д.С., Васяева Т.А., Хрюкин Е.А., Папакина А.В., 20252025Швороб Д.С., Васяева Т.А., Хрюкин Е.А., Папакина А.В.Shvorob D.S., Vasyaeva T.A., Khriukin E.A., Papakina A.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.sibjcem.ru/jour/article/view/2746

Развитие цифровых технологий и алгоритмов компьютерного зрения расширяет возможности использования искусственного интеллекта (ИИ) в патологической анатомии. Нейросети, основанные на глубоком обучении (ГО), успешно разрабатываются и применяются для выполнения задач, связанных с диагностикой и классификацией опухолей, идентификацией иммуногистохимических маркеров, морфометрии. Применение ИИ не только способствует объективизации диагностического процесса, но и снижает нагрузку на врача-патологоанатома, позволяя концентрироваться на более сложных случаях. Несмотря на это, существуют ограничения внедрения нейросетей в рутинную патологоанатомическую практику, включающие финансовые и юридические трудности, а также недоверчивое отношение к автоматической диагностике у врачей и пациентов. В обзоре литературы представлена информация об ИИ, машинном обучении (МО) и архитектурах нейросетей, а также об их интеграции в практику врача-патологоанатома. Перечислены программные продукты, использующиеся для количественных морфологических исследований, диагностики и прогнозирования заболеваний. Совокупность разработанных программ на основе ИИ указывает на значительный интерес и актуальность их использования в патологоанатомической практике и открывает новые границы в персонифицированной медицине.

 

The development of digital technologies and computer vision algorithms extends the possibilities of Artificial Intelligence application in pathology. Neural networks based on deep learning are being successfully developed and used to perform tasks related to the diagnosis and classification of tumors, identification of immunohistochemical markers and morphometry. The use of Artificial Intelligence not only contributes to the objectification of the diagnostic process, but also reduces the burden on the pathologists, allowing them to concentrate on more complex cases. Despite this, there are limitations to the introduction of neural networks into routine pathology practice, including financial and legal difficulties, as well as a distrustful attitude towards automatic diagnosis among doctors and patients. The literature review provides information on Artificial Intelligence, machine learning and neural network architecture, as well as their integration into the practice of a pathologist. The software products used for quantitative morphological studies, diagnosis and prognosis of diseases are listed. The set of developed AI-based programs indicates a significant interest and relevance of their use in pathological and anatomical practice and opens new frontiers in personalized medicine.

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