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<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">Digital Diagnostics</journal-id><journal-title-group><journal-title xml:lang="en">Digital Diagnostics</journal-title><trans-title-group xml:lang="ru"><trans-title>Digital Diagnostics</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>Digital Diagnostics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2712-8490</issn><issn publication-format="electronic">2712-8962</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">569388</article-id><article-id pub-id-type="doi">10.17816/DD569388</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Original Study Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Оригинальные исследования</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>原创性科研成果</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">Improving aortic aneurysm detection with artificial intelligence based on chest computed tomography data</article-title><trans-title-group xml:lang="ru"><trans-title>Вклад систем искусственного интеллекта в улучшение выявления аневризм аорты по данным компьютерной томографии грудной клетки</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>人工智能系统对从胸部计算机断层扫描数据中改进主动脉瘤检测的贡献</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4485-2638</contrib-id><contrib-id contrib-id-type="spin">9654-4005</contrib-id><name-alternatives><name xml:lang="en"><surname>Solovev</surname><given-names>Alexander V.</given-names></name><name xml:lang="ru"><surname>Соловьёв</surname><given-names>Александр Владимирович</given-names></name><name xml:lang="zh"><surname>Solovev</surname><given-names>Alexander V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>atlantis.92@mail.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0208-5218</contrib-id><contrib-id contrib-id-type="spin">4458-5608</contrib-id><name-alternatives><name xml:lang="en"><surname>Vasilev</surname><given-names>Yuriy A.</given-names></name><name xml:lang="ru"><surname>Васильев</surname><given-names>Юрий Александрович</given-names></name><name xml:lang="zh"><surname>Vasilev</surname><given-names>Yuriy A.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>канд. мед. наук</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine)</p></bio><email>VasilevYA1@zdrav.mos.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5649-2193</contrib-id><contrib-id contrib-id-type="spin">8449-6590</contrib-id><name-alternatives><name xml:lang="en"><surname>Sinitsyn</surname><given-names>Valentin E.</given-names></name><name xml:lang="ru"><surname>Синицын</surname><given-names>Валентин Евгеньевич</given-names></name><name xml:lang="zh"><surname>Sinitsyn</surname><given-names>Valentin E.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><email>vsini@mail.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff3"/><xref ref-type="aff" rid="aff4"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1694-4682</contrib-id><contrib-id contrib-id-type="spin">6193-1656</contrib-id><name-alternatives><name xml:lang="en"><surname>Petraikin</surname><given-names>Alexey V.</given-names></name><name xml:lang="ru"><surname>Петряйкин</surname><given-names>Алексей Владимирович</given-names></name><name xml:lang="zh"><surname>Petraikin</surname><given-names>Alexey V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>д-р мед. наук</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine)</p></bio><email>atlantis.92@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2990-7736</contrib-id><contrib-id contrib-id-type="spin">3602-7120</contrib-id><name-alternatives><name xml:lang="en"><surname>Vladzymyrskyy</surname><given-names>Anton V.</given-names></name><name xml:lang="ru"><surname>Владзимирский</surname><given-names>Антон Вячеславович</given-names></name><name xml:lang="zh"><surname>Vladzymyrskyy</surname><given-names>Anton V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine)</p></bio><bio xml:lang="ru"><p>д-р мед. наук</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine)</p></bio><email>VladzimirskijAV@zdrav.mos.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7613-5273</contrib-id><contrib-id contrib-id-type="spin">5266-0618</contrib-id><name-alternatives><name xml:lang="en"><surname>Shulkin</surname><given-names>Igor M.</given-names></name><name xml:lang="ru"><surname>Шулькин</surname><given-names>Игорь Михайлович</given-names></name><name xml:lang="zh"><surname>Shulkin</surname><given-names>Igor M.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>ShulkinIM@zdrav.mos.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5792-3912</contrib-id><contrib-id contrib-id-type="spin">1811-7595</contrib-id><name-alternatives><name xml:lang="en"><surname>Sharova</surname><given-names>Daria E.</given-names></name><name xml:lang="ru"><surname>Шарова</surname><given-names>Дарья Евгеньевна</given-names></name><name xml:lang="zh"><surname>Sharova</surname><given-names>Daria E.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>SharovaDE@zdrav.mos.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4293-2514</contrib-id><contrib-id contrib-id-type="spin">2278-7290</contrib-id><name-alternatives><name xml:lang="en"><surname>Semenov</surname><given-names>Dmitry S.</given-names></name><name xml:lang="ru"><surname>Семенов</surname><given-names>Дмитрий Сергеевич</given-names></name><name xml:lang="zh"><surname>Semenov</surname><given-names>Dmitry S.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Cand. Sci. (Engineering)</p></bio><bio xml:lang="ru"><p>канд. техн. наук</p></bio><bio xml:lang="zh"><p>Cand. Sci. (Engineering)</p></bio><email>SemenovDS4@zdrav.mos.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies</institution></aff><aff><institution xml:lang="ru">Научно-практический клинический центр диагностики и телемедицинских технологий</institution></aff><aff><institution xml:lang="zh">Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Morozov Children’s Municipal Clinical Hospital</institution></aff><aff><institution xml:lang="ru">Морозовская детская городская клиническая больница</institution></aff><aff><institution xml:lang="zh">Morozov Children’s Municipal Clinical Hospital</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Clinical City Hospital named after I.V. Davydovsky</institution></aff><aff><institution xml:lang="ru">Городская клиническая больница имени И.В. Давыдовского</institution></aff><aff><institution xml:lang="zh">Clinical City Hospital named after I.V. Davydovsky</institution></aff></aff-alternatives><aff-alternatives id="aff4"><aff><institution xml:lang="en">Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">Московский государственный университет имени М.В. Ломоносова</institution></aff><aff><institution xml:lang="zh">Lomonosov Moscow State University</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2024-03-11" publication-format="electronic"><day>11</day><month>03</month><year>2024</year></pub-date><pub-date date-type="pub" iso-8601-date="2024-04-19" publication-format="electronic"><day>19</day><month>04</month><year>2024</year></pub-date><volume>5</volume><issue>1</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><issue-title xml:lang="zh"/><fpage>29</fpage><lpage>40</lpage><history><date date-type="received" iso-8601-date="2023-09-19"><day>19</day><month>09</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2023-12-19"><day>19</day><month>12</month><year>2023</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Эко-вектор</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2024, Eco-Vector</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">Эко-вектор</copyright-holder><copyright-holder xml:lang="zh">Eco-Vector</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc-nd/4.0/</ali:license_ref></license></permissions><self-uri xlink:href="https://jdigitaldiagnostics.com/DD/article/view/569388">https://jdigitaldiagnostics.com/DD/article/view/569388</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND: </bold>Aortic aneurysms are known as “silent killers” because this potentially fatal condition can be asymptomatic. The annual incidence of thoracic aortic aneurysms and ruptures is approximately 10 and 1.6 per 100,000 individuals, respectively. The mortality rate for ruptured aneurysms ranges from 94% to 100%. Early diagnosis and treatment can be life-saving. Artificial intelligence technologies can significantly improve diagnostic accuracy and save the lives of patients with thoracic aortic aneurysms.</p> <p><bold>AIM: </bold>This study aimed to assess the efficacy of artificial intelligence technologies for detecting thoracic aortic aneurysms on chest computed tomography scans, as well as the possibility of using artificial intelligence as a clinical decision support system for radiologists during the primary interpretation of radiological images.</p> <p><bold>MATERIALS AND METHODS: </bold>The results of using artificial intelligence technologies for detecting thoracic aortic aneurysms on non-contrast chest computed tomography scans were evaluated. A sample of 84,405 patients &gt;18 years old was generated, with 86 cases of suspected thoracic aortic aneurysms based on artificial intelligence data selected and retrospectively assessed by radiologists and vascular surgeons. To assess the age distribution of the aortic diameter, an additional sample of 968 cases was randomly selected from the total number.</p> <p><bold>RESULTS: </bold>In 44 cases, aneurysms were initially identified by radiologists, whereas in 31 cases, aneurysms were not detected initially; however, artificial intelligence aided in their detection. Six studies were excluded, and five studies had false-positive results. Artificial intelligence aids in detecting and highlighting aortic pathological changes in medical images, increasing the detection rate of thoracic aortic aneurysms by 41% when interpreting chest computed tomography scans. The use of artificial intelligence technologies for primary interpretations of radiological studies and retrospective assessments is advisable to prevent underdiagnosis of clinically significant pathologies and improve the detection rate of pathological aortic enlargement. In the additional sample, the incidence of thoracic aortic dilation and thoracic aortic aneurysms in adults was 14.5% and 1.2%, respectively. The findings also revealed an age-dependent diameter of the thoracic aorta in both men and women.</p> <p><bold>CONCLUSION: </bold>The use of artificial intelligence technologies in the primary interpretation of chest computed tomography scans can improve the detection rate of clinically significant pathologies such as thoracic aortic aneurysms. Expanding retrospective screening based on chest computed tomography scans using artificial intelligence can improve the diagnosis of concomitant pathologies and prevent negative consequences.</p></abstract><trans-abstract xml:lang="ru"><p><bold>Обоснование.</bold> Аневризмы аорты — «тихие убийцы», развиваются без симптомов и могут привести к летальному исходу. Ежегодно заболеваемость аневризмой грудной аорты составляет около 10 случаев на 100 000 человек, а частота разрывов аневризмы — около 1,6 случая. Ранняя диагностика и лечение могут спасти жизнь пациента. Использование технологий искусственного интеллекта может значительно улучшить качество диагностики и предотвратить летальный исход.</p> <p><bold>Цель</bold><bold> — </bold>оценить эффективность применения технологий искусственного интеллекта в выявлении аневризм грудного отдела аорты на компьютерной томографии органов грудной клетки и исследовать возможности использования этих технологий в качестве системы поддержки принятия врачебных решений врача-рентгенолога при первичном описании лучевых исследований.</p> <p><bold>Материалы </bold><bold>и </bold><bold>методы.</bold><bold> </bold>Были оценены результаты использования технологий искусственного интеллекта для выявления аневризмы грудной аорты на компьютерной томографии органов грудной клетки без контрастного усиления. Была сформирована выборка из 84 405 случаев обследования пациентов старше 18 лет, из которых отобрано и ретроспективно пересмотрено сосудистыми хирургами Научно-исследовательского института скорой помощи имени Н.В. Склифосовского 86 исследований с подозрением на наличие аневризмы грудного отдела аорты по данным технологий искусственного интеллекта. Эти исследования были также ретроспективно оценены двумя врачами-рентгенологами.</p> <p>Была сформирована дополнительная выборка из 968 исследований, взятых в случайном порядке из общего числа, для оценки корреляции возраста пациентов и диаметра грудного отдела аорты.</p> <p><bold>Результаты.</bold> Анализ показал, что в 44 исследованиях аневризма была первично выявлена врачом-рентгенологом, в 31 случае аневризмы не были описаны, но технология искусственного интеллекта помогла выявить патологию. Ещё 6 исследований были исключены из выборки, а в 5 случаях были обнаружены ложноположительные результаты анализа.</p> <p>Использование технологий искусственного интеллекта обнаруживает и выделяет патологические изменения аорты на медицинских изображениях, тем самым повышая выявляемость аневризмы грудной аорты при интерпретации результатов компьютерной томографии органов грудной клетки на 41%. При первичном описании лучевых исследований и в ретроспективных исследованиях целесообразно использовать технологии искусственного интеллекта для профилактики пропусков клинически значимых патологий — как в качестве системы поддержки принятия врачебных решений для врача-рентгенолога, так и для повышения выявляемости патологического расширения грудного отдела аорты.</p> <p>По дополнительной выборке в популяции взрослого населения частота дилатации грудного отдела аорты составила 14,5%, а аневризм грудного отдела аорты —1,2%. Данные также показали возрастную зависимость диаметра грудного отдела аорты для мужчин и женщин.</p> <p><bold>Заключение.</bold> Применение технологий искусственного интеллекта в процессе первичного описания результатов компьютерной томографии органов грудной клетки может повысить выявляемость клинически значимых патологических состояний, таких как аневризма грудного отдела аорты. Расширение ретроспективного скрининга по данным компьютерной томографии органов грудной клетки с использованием технологий искусственного интеллекта может улучшить качество диагностики сопутствующих патологий и предотвратить негативные последствия для пациентов.</p></trans-abstract><trans-abstract xml:lang="zh"><p>论证。主动脉瘤是“无声杀手”，发病时没有任何症状，而且可能致命。胸主动脉瘤的年发病率约为每10万人10例，动脉瘤破裂的发病率约为1.6例。早期诊断和治疗可以挽救患者的生命。人工智能技术的使用可以大大提高诊断质量，防止死亡。</p> <p>目的。本研究的目的是评估人工智能技术在胸部计算机断层扫描中检测胸主动脉瘤的有效性，并探讨这些技术作为放射科医生临床决策支持系统在放射学检查初步描述中的可行性。</p> <p>材料与方法。对使用人工智能技术在无对比度增强的胸部计算机断层扫描中检测胸主动脉瘤的结果进行了评估。研究人员对84405名18岁以上的患者进行了抽样检查。通过人工智能技术筛选出86个疑似胸主动脉瘤的检查。俄罗斯N.V.斯克利福索夫斯基急救研究所的血管外科医生对这些检查结果进行了回顾性分析。两名放射科医生也对这些检查进行了回顾性评估。</p> <p>另外从总数中随机抽取，形成了包括968个检查在内的额外样本以评估患者年龄与胸主动脉直径之间的相关性。</p> <p>结果。分析表明，在44例检查中，动脉瘤最初是由放射科医生检测到的；在31例检查中，动脉瘤未被描述，但人工智能技术帮助确定了病理。另有6例检查被排除在样本之外，而有5例检查发现了假阳性检测结果。</p> <p>使用人工智能技术可以检测并突出显示医学图像中主动脉的病理变化。因此，在解读胸部计算机断层扫描结果时发现胸主动脉瘤的概率提高了41%。在放射学研究的初步描述和回顾性研究中，使用人工智能技术来防止遗漏具有临床意义的病理是可行的，既可作为放射科医生的医疗决策支持系统，又可提高胸主动脉病理扩张的可探测性。</p> <p>在另一个成年人群样本中，胸主动脉扩张的发生率为14.5%，胸主动脉瘤的发生率为1.2%。数据还显示了，男性和女性的胸主动脉直径与年龄有关。</p> <p>结论。将人工智能技术应用于胸部器官CT结果的初步描述过程中，可以提高对胸主动脉瘤等临床重大病理状态的检测。利用人工智能技术扩大胸部计算机断层扫描的回顾性筛查范围，可提高合并症的诊断质量，避免给患者带来不良后果。</p></trans-abstract><kwd-group xml:lang="en"><kwd>computed tomography</kwd><kwd>aortic aneurysm</kwd><kwd>artificial intelligence</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>компьютерная томография</kwd><kwd>аневризма аорты</kwd><kwd>искусственный интеллект</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>电子计算机断层扫描</kwd><kwd>主动脉瘤</kwd><kwd>人工智能</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="en">Government of the Russian Federation</institution></institution-wrap><institution-wrap><institution xml:lang="ru">Правительство РФ</institution></institution-wrap><institution-wrap><institution xml:lang="zh">Government of the Russian Federation</institution></institution-wrap></funding-source><award-id>№ 123031400009-1</award-id></award-group></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">The top 10 causes of death [Internet]. 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