<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Journal of Experimental and Clinical Surgery</journal-id><journal-title-group><journal-title xml:lang="en">Journal of Experimental and Clinical Surgery</journal-title><trans-title-group xml:lang="ru"><trans-title>Вестник экспериментальной и клинической хирургии</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2070-478X</issn><issn publication-format="electronic">2409-143X</issn><publisher><publisher-name xml:lang="en">Voronezh State Medical University named after N.N. Burdenko</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">1747</article-id><article-id pub-id-type="doi">10.18499/2070-478X-2024-17-4-209-216</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Review of literature</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Обзор литературы</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Potentials of Artificial Intelligence in Assessing Pancreatic Pathology Based on Spiral Computed Tomography Findings</article-title><trans-title-group xml:lang="ru"><trans-title>Возможности искусственного интеллекта при оценке патологии поджелудочной железы по данным спиральной компьютерной томографии</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4556-4913</contrib-id><contrib-id contrib-id-type="spin">5571-8893</contrib-id><name-alternatives><name xml:lang="en"><surname>Sigua</surname><given-names>Badri V.</given-names></name><name xml:lang="ru"><surname>Сигуа</surname><given-names>Бадри Валериевич</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>M.D., Head of the Department of General Surgery, Faculty of Medicine, Institute of Medical Education</p></bio><bio xml:lang="ru"><p>доктор медицинских наук, заведующий кафедрой общей хирургии лечебного факультета Института медицинского образования</p></bio><email>dr.sigua@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0001-1362-7916</contrib-id><name-alternatives><name xml:lang="en"><surname>Kleymyuk</surname><given-names>Sofya V.</given-names></name><name xml:lang="ru"><surname>Клеймюк</surname><given-names>Софья Викторовна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Assistant of the Department of General Surgery, Faculty of Medicine, Institute of Medical Education</p></bio><bio xml:lang="ru"><p>ассистент кафедры общей хирургии лечебного факультета Института медицинского образования</p></bio><email>sofikleim@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2070-7420</contrib-id><contrib-id contrib-id-type="spin">2649-1050</contrib-id><name-alternatives><name xml:lang="en"><surname>Zakharov</surname><given-names>Evgeny A.</given-names></name><name xml:lang="ru"><surname>Захаров</surname><given-names>Евгений Алексеевич</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Ph.D., Assistant of the Department of Faculty Surgery with the course of Endoscopy named after I.I. Grekov</p></bio><bio xml:lang="ru"><p>кандидат медицинских наук, ассистент кафедры факультетской хирургии с курсом эндоскопии им. И.И. Грекова</p></bio><email>dr.zakharovea@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5608-3544</contrib-id><contrib-id contrib-id-type="spin">6826-0184</contrib-id><name-alternatives><name xml:lang="en"><surname>Semenova</surname><given-names>Evgeniya A.</given-names></name><name xml:lang="ru"><surname>Семенова</surname><given-names>Евгения Анатольевна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Ph.D., Associate Professor of the Department of Biotechnical Systems</p></bio><bio xml:lang="ru"><p>кандидат технических наук, доцент кафедры биотехнических систем</p></bio><email>easemenova@etu.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0004-5870-7225</contrib-id><name-alternatives><name xml:lang="en"><surname>Loginova</surname><given-names>Diana D.</given-names></name><name xml:lang="ru"><surname>Логинова</surname><given-names>Диана Дмитриевна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>undergraduate student, Department of Biotechnical Systems</p></bio><bio xml:lang="ru"><p>магистрант, кафедры биотехнических систем</p></bio><email>logidi@mail.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2329-0023</contrib-id><name-alternatives><name xml:lang="en"><surname>Zemlyanoy</surname><given-names>Vyacheslav P.</given-names></name><name xml:lang="ru"><surname>Земляной</surname><given-names>Вячеслав Петрович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>M.D., Head of the Department of Faculty Surgery with the course of endoscopy named after I.I. Grekov</p></bio><bio xml:lang="ru"><p>доктор медицинских наук, заведующий кафедрой факультетской хирургии с курсом эндоскопии им. И.И. Грекова</p></bio><email>vyacheslav.zemlyanoy@szgmu.ru</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Almazov National Medical Research Centre</institution></aff><aff><institution xml:lang="ru">Национальный медицинский исследовательский центр имени В.А. Алмазова</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">North-Western State Medical University named after I.I. Mechnikov</institution></aff><aff><institution xml:lang="ru">Северо-Западный государственный медицинский университет имени И.И. Мечникова</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Saint Petersburg Electrotechnical University LETI</institution></aff><aff><institution xml:lang="ru">Санкт-Петербургский государственный электротехнический университет "ЛЭТИ" им. В.И.Ульянова (Ленина)</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-12-11" publication-format="electronic"><day>11</day><month>12</month><year>2024</year></pub-date><volume>17</volume><issue>4</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>209</fpage><lpage>216</lpage><history><date date-type="received" iso-8601-date="2023-10-25"><day>25</day><month>10</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2024-11-17"><day>17</day><month>11</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Voronezh N.N. Burdenko State Medical University</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, ФГБОУ ВО ВГМУ им. Н.Н. Бурденко Минздрава России</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Voronezh N.N. Burdenko State Medical University</copyright-holder><copyright-holder xml:lang="ru">ФГБОУ ВО ВГМУ им. Н.Н. Бурденко Минздрава России</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2027-12-11"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://vestnik-surgery.com/journal/about/editorialPolicies</ali:license_ref></license></permissions><self-uri xlink:href="https://vestnik-surgery.com/journal/article/view/1747">https://vestnik-surgery.com/journal/article/view/1747</self-uri><abstract xml:lang="en"><p>Artificial intelligence is the study of algorithms that give machines the ability to "reason" and acquire cognitive functions to achieve human–level performance in cognition-related tasks such as, for example, problem solving, object and word recognition, and decision-making. Currently, there are a lot of studies proving that artificial intelligence can not only diagnose diseases on a par with doctors, but also spend much less time on it. Artificial intelligence has entered many areas of medicine, and recently its role has become more significant in the diagnosis and treatment of pancreatic pathology.</p> <p>Over the past decade, the number and variation of methods for analyzing medical images has increased significantly due to the development of artificial intelligence, new programs for analyzing and systematizing objects.</p> <p><bold>The</bold><bold> </bold><bold>aim</bold><bold> </bold><bold>of</bold><bold> </bold><bold>this</bold><bold> </bold><bold>review</bold> is to analyze, summarize and evaluate data published in the scientific literature on the use of artificial intelligence techniques to diagnose pancreatic pathology based on the results of computed tomography. It is demonstrated further perspectives and the need to develop this area in medical practice.</p> <p>A systematic literature search was conducted on the databases of the journals PubMed and eLibrary. The search for literature was carried out by Keywords"artificial intelligence", "pancreas", "computed tomography", "radiomics". The search interval was 2015-2023. The authors investigated all research studies of foreign and Russian scientists, which contain information on the use of diverse options of artificial intelligence techniques for differential diagnosis of pancreatic pathology, mainly based on computed tomography, and their assessment to demonstrate their further beneficial development in the field of medicine.</p> <p>To date, artificial intelligence programs based on spiral computed tomography data allow differentiating the pathology of the pancreas with high accuracy, which greatly facilitates human efforts and allows applying them as an indispensable assistant in work. That is why it is necessary to introduce these technologies into the circulation of medical institutions as actively as possible in order to expand the database of artificial intelligence, which will achieve more accurate results in the diagnosis of pancreatic diseases and more.</p></abstract><trans-abstract xml:lang="ru"><p>Искусственный интеллект – это изучение алгоритмов, которые дают машинам способность «рассуждать» и приобретать когнитивные функции для достижения производительности человеческого уровня в задачах, связанных с познанием, таких как, например, решение проблем, распознавание объектов и слов, принятие решений. В настоящее время имеется масса исследований, доказывающих, что искусственный интеллект не только наравне с врачами может проводить диагностику заболеваний, но и тратить на это гораздо меньший временной ресурс. Искусственный интеллект вошел во многие сферы медицины и, в последнее время, его роль стала более весомой в диагностике и лечении патологии поджелудочной железы.</p> <p>За последнее десятилетие значительно увеличилось количество и вариация методов анализа медицинских изображений в связи с развитием искусственного интеллекта, новых программ для анализа и систематизации объектов.</p> <p><bold>Целью</bold> данного обзора является анализ, обобщение и оценка данных, которые были опубликованы в научной литературе об использовании методик искусственного интеллекта для диагностики патологии поджелудочной железы по результатам компьютерной томографии. Демонстрация дальнейших перспектив и необходимости развития данного направления в медицинской практике.</p> <p>Систематический поиск литературы проведен по базам данных журналов «PubMed» и «eLibrary». Поиск литературы в журналах проводился по ключевым словам: «искусственный интеллект», «поджелудочная железа», «компьютерная томография», «радиомика». Интервал поиска — 2015–2023 гг. Были изучены все работы зарубежных и отечественных авторов, которые несут в себе информацию об использовании различных вариаций методик искусственного интеллекта для дифференциальной диагностики патологии поджелудочной железы, преимущественно по данным компьютерной томографии, а также их оценка для демонстрации преимущества дальнейшего развития в области медицины.</p> <p>На сегодняшний день программы искусственного интеллекта по данным спиральной компьютерной томографии позволяют дифференцировать патологию поджелудочной железы с высокой точностью, что значительно облегчает человеческие усилия и служит незаменимым помощником в работе. Именно поэтому необходимо как можно активнее внедрять данные технологии в оборот медицинских учреждений для того, чтобы расширять базу данных искусственного интеллекта, что позволит добиться более точных результатов в диагностике заболеваний поджелудочной железы и не только.</p></trans-abstract><kwd-group xml:lang="en"><kwd>artificial intelligence</kwd><kwd>diagnostics</kwd><kwd>pancreas</kwd><kwd>spiral computed tomography, radiomics</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>диагностика</kwd><kwd>поджелудочная железа</kwd><kwd>спиральная компьютерная томография, радиомика</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">Mel'nikov PV, Dovedov VN, Kanner DYu, Chernikovskii IL. Artificial intelligence in oncosurgical practice. Tazovaya khirurgiya i onkologiya. 2020; 10: 3–4: 60–64. (in Russ.)</mixed-citation><mixed-citation xml:lang="ru">Мельников П. В., Доведов В. Н., Каннер Д. Ю., Черниковский И. Л. Искусственный интеллект в онкохирургической практике. Тазовая хирургия и онкология. 2020; 10: 3–4: 60–64.</mixed-citation></citation-alternatives></ref><ref id="B2"><label>2.</label><mixed-citation>Bektas M, Zonderhuis BM, Marquering HA, Pereira JC, Burchell GL, Peet DL. Artificial intelligence in hepatopancreaticobiliary surgery: a systematic review. Artificial Intelligence Surgery. 2022; 2: 4: 1–12.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Elyan E, Vuttipittayamongkol P, Johnston P, Martin K, McPherson K, Francisco Moreno García K, Jayne C, Sarker MK. Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial Intelligence Surgery. 2022; 2: 1: 24–45.</mixed-citation></ref><ref id="B4"><label>4.</label><citation-alternatives><mixed-citation xml:lang="en">Litvin AA, Burkin DA, Kropinov AA, Paramfin FN. Radiomika i analiz tekstur tsifrovykh izobrazhenii v onkologii (obzor). Sovremennye tekhnologii v meditsine. 2021; 13: 2: 97–106. (in Russ.)</mixed-citation><mixed-citation xml:lang="ru">Литвин А.А., Буркин Д.А., Кропинов А.А., Парамфин Ф.Н. Радиомика и анализ текстур цифровых изображений в онкологии (обзор). Современные технологии в медицине. 2021; 13: 2: 97–106.</mixed-citation></citation-alternatives></ref><ref id="B5"><label>5.</label><citation-alternatives><mixed-citation xml:lang="en">Fedorov AV, Ektov VN, Khodorkovsky MA, Skorynin OS. Potential of Minimally Invasive Drainage Interventions for Acute Pancreatitis. Journal of Experimental and Clinical Surgery. 2022;15(2):165-173. doi: 10.18499/2070-478X-2022-15-2-165-173 (in Russ.)</mixed-citation><mixed-citation xml:lang="ru">Федоров А.В., Эктов В.Н., Ходорковский М.А., Скорынин О.С. Варианты миниинвазивных дренирующих вмешательств при остром панкреатите. Вестник экспериментальной и клинической хирургии. 2022;15(2):165-173. doi: 10.18499/2070-478X-2022-15-2-165-173</mixed-citation></citation-alternatives></ref><ref id="B6"><label>6.</label><mixed-citation>Kroner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El- Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol. 2021; 28: 27: 40: 6794–6824.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Langan RC, Pitt HA, Schneider E. Role of artificial intelligence in pancreatic cystic neoplasms: modernizing the identification and longitudinal management of pancreatic cysts. Artificial Intelligence Surgery. 2023; 3: 3: 140–146.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LS. A primer on artificial intelligence in pancreatic imaging. Diagnostic and Interventional Imaging. 2023; 104: 9: 435– 447.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers. 2022; 14: 21: 5382.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Kumar, U. In Research Anthology on Artificial Intelligence Applications in Security Information Resources. Management Association. 2020; 1052–1084.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, Braren R. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP). Journal of Clinical Medicine. 2020; 9: 12: 4013.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdominal Radiology (NY). 2020; 45: 5: 1524—1533.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019; 9: 5: 1303–1322.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016; 278: 563–577.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Park S, Sham JG, Kawamoto S, Blair AB, Rozich N, Fouladi DF, Shayesteh S, Hruban RH, He J, Wolfgang CL, Yuille AL, Fishman EK, Chu LC. CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma. American Journal of Roentgenology. 2021; 217; 5: 1104—1112.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Chetan MR, Gleeson FV. Radiomics in predicting treatment response in nonsmall-cell lung cancer: current status, challenges and future perspectives. Eur. Radiol. 2021; 31: 2: 1049–1058.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Ibrahim A, Primakov S, Woodruff HC, Halilaj I, Refaee T, Granzier R, Widaatalla Y, Hustinx R, Mottaghy FM, Lambin P. Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework. Methods. 2021; 188: 20–29.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 2012; 72: 4875–4882.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Marti-Bonmati L, Cerda-Alberich L, Perez-Girbes A, Díaz Beveridge R, Montalva Oron E, Perez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol. 2022; 1; 95: 1137.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Xianze W, Yuan CW, Elon C, Yi Z, Eyad I, Ashley RD. The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer. Journal of Pancreatology. 2020; 3: 4: 173—180.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Gai T, Jo J, Zheng B, Thai T, Jones M. Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images. Journal of X-Ray Science and Technology. 2022; 30: 377–388.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Fiagbedzi EW, Gorleku PhN, Nyarko S, Atuwo-Ampoh VD, Fiagan YaAC, Asare A. The Role of Artificial Intelligence (AI) in Radiation Protection of Computed Tomography and Fluoroscopy: A Review. Open Journal of Medical Imaging. 2022; 12: 1: 25–36.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Liu SL, Guo YT, Zhou YP, Zhang ZD, Li S, Lu Y. Establishment and Application of an Artificial Intelligence Diagnosis System for Pancreatic Cancer with a Faster Region-Based Convolutional Neural Network. Chin. Med. J. 2019; 32: 23: 2795–2803.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Zhang MM, Yang H, Jin ZD, Yu JG, Cai ZY, Li ZS. Differential Diagnosis of Pancreatic Cancer from Normal Tissue with Digital Imaging Processing and Pattern Recognition Based on a Support Vector Machine of EUS Images. Gastrointest. Endosc. 2010; 72: 5: 978–985.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Saif MW. Pancreatic neoplasm in 2011: an update. JOP. 2011; 12: 4: 316–321.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Li J, Lu J, Liang P, Li А, Hu Y, Shen Y, Hu D, Li Z. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Medicine. 2018; 7: 10: 4924—4931.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Li S, Jiang H, Wang Z, Zhang G, Yao YD. An effective computer aided diagnosis model for pancreas cancer on PET/CT images. Comput Methods Programs Biomed. 2018; 165: 205–214.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Vilas-Boas F, Ribeiro T, Afonso J. Deep learning for automatic differentiation of mucinous versus non-mucinous pancreatic cystic lesions: a pilot study. Diagnostics. 2022; 12: 9: 2041.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol (NY). 2022; 47: 11: 3806–3816.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Kooragayala K, Crudeli C, Kalola A, et al. Utilization of natural language processing software to identify worrisome pancreatic lesions. Ann Surg Oncol. 2022; 29: 13: 8513–8519.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Roch AM, Mehrabi S, Krishnan A, Schmidt HE, Kesterson J, Beesley C, Dexter PR, Palakal M, Schmidt CM. Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer. HPB (Oxford). 2015; 17: 5: 447–53.</mixed-citation></ref><ref id="B32"><label>32.</label><citation-alternatives><mixed-citation xml:lang="en">Paramzin FN, Kakotkin VV, Burkin DA, Agapov MA. Radionics and artificial intelligence in the differential diagnosis of tumor and non-tumor formations of the pancreas (review). Khirurgicheskaya praktika. 2023; 1: 53–65.</mixed-citation><mixed-citation xml:lang="ru">Парамзин ФН, Какоткин ВВ, Буркин ДА, Агапов МА. Радиомика и искусственный интеллект в дифференциальной диагностике опухолевых и неопухолевых образований поджелудочной железы (обзор). Хирургическая практика. 2023; 1: 53–65.</mixed-citation></citation-alternatives></ref><ref id="B33"><label>33.</label><mixed-citation>Casa C, D’Aviero A, Cusumano D, Romano A, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L, Piras A, Preziosi F, Mariani S, Boskoski I. The impact of radiomics in diagnosis and staging of pancreatic cancer. Therapeutic Advances in Gastrointestinal Endoscopy. 2022; 15.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action. Cancer Lett. 2020; 28; 469: 228–237.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Baebler B, Gotz M, Antoniades C, Heidenreich JF, Leiner T, Beer M. Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective. Front Cardiovasc Med. 2023; 16; 10.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Ng D, Du H, Yao MM, Kosik RO, Chan WP, Feng M. Today radiologists meet tomorrow AI: the promises, pitfalls, and unbridled potential. Quant Imaging Med Surg. 2021; 11: 6: 2775– 2779.</mixed-citation></ref></ref-list></back></article>
