spracticeХирургическая практикаSurgical practice (Russia)2223-2427АНО "Консорциум "Медицинская техника"10.38181/2223-2427-2023-1-5spractice-387Research ArticleХИРУРГИЯSURGERYРадиомика и искусственный интеллект в дифференциальной диагностике опухолевых и неопухолевых заболеваний поджелудочной железы (обзор)Radiomics and artificial intelligence in the differential diagnosis of tumor and non-tumor diseases of the pancreas. Reviewhttps://orcid.org/0000-0003-1465-7428ПарамзинФ. Н.ParamzinF. N.

Парамзин Федор Николаевич - аспирант.

236041, Калининград, ул. А. Невского, 14

Fedor N. Paramzin - PhD student, Immanuel Kant Baltic Federal University.

A. Nevskogo St. 14, Kaliningrad, 236041

fedia931@yandex.ruhttps://orcid.org/0000-0003-0352-2317КакоткинВ. В.KakotkinV. V.

Какоткин Виктор Викторович - ассистент кафедры хирургических дисциплин высшей школы медицины.

236041, Калининград, ул. А. Невского, 14

Viktor V. Kakotkin - Assistant professor, Department of Surgical Disciplines, Higher School of Medicine, Immanuel Kant Baltic Federal University.

A. Nevskogo St. 14, Kaliningrad, 236041

Vkakotkin@kantiana.ruhttps://orcid.org/0000-0002-4393-6170БуркинД. А.BurkinD. A.

Буркин Дмитрий Андреевич - аспирант.

236041, Калининград, ул. А. Невского, 14

Dmitry A. Burkin - PhD student, Immanuel Kant Baltic Federal University.

A. Nevskogo St. 14, Kaliningrad, 236041

dima.burkin96@yandex.ruhttps://orcid.org/0000-0002-6569-7078АгаповМ. A.AgapovM. A.

Агапов Михаил Андреевич - доктор медицинских наук, профессор, руководитель ОНК «Институт медицины и наук о жизни».

236041, Калининград, ул. А. Невского, 14

Mikhail A. Agapov - Head of the Scientific and Education Centre “Institute of Medicine and Life Sciences”, Immanuel Kant Baltic Federal University.

A. Nevskogo St. 14, Kaliningrad, 236041

getinfo911@mail.ruБалтийский федеральный университет им. И. КантаРоссияImmanuel Kant Baltic Federal UniversityRussian Federation202303052023015365Copyright © Парамзин Ф.Н., Какоткин В.В., Буркин Д.А., Агапов М.A., 20232023Парамзин Ф.Н., Какоткин В.В., Буркин Д.А., Агапов М.A.Paramzin F.N., Kakotkin V.V., Burkin D.A., Agapov M.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.spractice.ru/jour/article/view/387

Работа основана на анализе данных литературы, посвященной внедрению радиомического анализа и искусственного интеллекта (ИИ) в диагностику заболеваний поджелудочной железы, за последние 5 лет. Главная цель обзора — определить наиболее перспективные методы радиомной диагностики и возможности применения искусственного интеллекта в диагностике заболеваний поджелудочной железы. Рассмотрены основные понятия радиомики, этапы радиомического анализа (сбор данных, предварительная обработка, сегментация опухоли, обнаружение и извлечение данных, моделирование, статистическая обработка, валидация данных), оценены возможности искусственного интеллекта и искусственных нейронных сетей в хирургической и онкологической панкреатологии. Описаны особенности и преимущества применения радиомического анализа и ИИ при диагностике и прогнозировании онкологических заболеваний поджелудочной железы. Отмечены ограничения, связанные с использованием радиомики и ИИ в панкреатологии.

This work provides a comprehensive overview of the recent advancements in the field of radiomic diagnostics and artificial intelligence (AI) in the diagnosis of pancreatic diseases. The integration of radiochemical analysis and AI has allowed for more accurate and precise diagnoses of pancreatic diseases, including pancreatic cancer. The review highlights the different stages of radiomic analysis, such as data collection, preprocessing, tumour segmentation, data detection and extraction, modeling, statistical processing, and data validation, which are essential for the accurate diagnosis of pancreatic diseases. Furthermore, the review evaluates the possibilities of using AI and artificial neural networks in surgical and oncological pancreatology. The features and advantages of using radiochemical analysis and AI in the diagnosis and prognosis of pancreatic cancer are also described. These advancements have the potential to improve patient outcomes, as early and accurate diagnosis can lead to earlier treatment and better chances of recovery. However, the limitations associated with the use of radiometry and AI in pancreatology are also noted, such as the lack of standardization and the potential for false positives or false negatives. Nevertheless, this work highlights the potential benefits of incorporating radiochemical analysis and AI in the diagnosis and treatment of pancreatic diseases, which can ultimately lead to better patient care and outcomes.

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