Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector64167910.17816/DD641679QHBRWFReviewsНаучные обзоры科学评论Review ArticleDiagnosis of thoracic aortic aneurysms and pathological pulmonary trunk dilation using chest computed tomography and artificial intelligence: modern approaches and prospects (a review)Диагностика аневризм грудного отдела аорты и патологического расширения лёгочного ствола с использованием компьютерной томографии органов грудной клетки и искусственного интеллекта: современные подходы и перспективы (научный обзор)使用胸部计算机断层扫描与人工智能技术诊断胸主动脉瘤与肺动脉干异常扩张：现代方法与前景展望（文献综述）https://orcid.org/0000-0003-4485-26389654-4005SolovevAlexander V.СоловьёвАлександр ВладимировичSolovevAlexander V.
Russian Federation

MD

MD

atlantis.92@mail.ruhttps://orcid.org/0000-0002-5649-21938449-6590SinitsynValentin E.СиницынВалентин ЕвгеньевичSinitsynValentin E.
Russian Federation

MD, Dr. Sci. (Medicine), Professor

д-р мед. наук, профессор

MD, Dr. Sci. (Medicine), Professor

vsini@mail.ruhttps://orcid.org/0000-0002-2990-77363602-7120VladzymyrskyyAnton V.ВладзимирскийАнтон ВячеславовичVladzymyrskyyAnton V.
Russian Federation

MD, Dr. Sci. (Medicine)

д-р мед. наук

MD, Dr. Sci. (Medicine)

VladzimirskijAV@zdrav.mos.ruhttps://orcid.org/0000-0002-0041-32815146-4355PamovaAnastasia P.ПамоваАнастасия ПетровнаPamovaAnastasia P.
Russian Federation

MD, Cand. Sci. (Medicine)

канд. мед. наук

MD, Cand. Sci. (Medicine)

PamovaAP@zdrav.mos.ruResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesНаучно-практический клинический центр диагностики и телемедицинских технологийResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesMorozov Children's Municipal Clinical HospitalМорозовская детская городская клиническая больницаMorozov Children's Municipal Clinical HospitalLomonosov Moscow State UniversityМосковский государственный университет имени М.В. ЛомоносоваLomonosov Moscow State University2805202508072025622863010711202420022025Copyright ©; 2025, Eco-VectorCopyright ©; 2025, Эко-векторCopyright ©; 2025, Eco-Vector2025Eco-VectorЭко-векторEco-Vectorhttps://creativecommons.org/licenses/by-nc-nd/4.0https://jdigitaldiagnostics.com/DD/article/view/641679

Early diagnosis of thoracic aortic aneurysms and pathological pulmonary trunk dilation is crucial to prevent severe complications, including vascular wall rupture and acute right ventricular failure, and reduce cardiovascular mortality. This review examines contemporary imaging approaches for these conditions, focusing on computed tomography as the gold standard modality. Emphasis was placed on the implementation of artificial intelligence technologies, which enable automatic segmentation of vascular structures, measurement of their diameter, and opportunistic screening, allowing early detection of asymptomatic conditions without additional diagnostic procedures, thereby reducing radiologist workload and improving medical care quality. The study comprehensively analyzed the Moscow Experiment, wherein the application of artificial intelligence in medical image analysis showed high sensitivity, reproducibility, and reduced reporting time. Despite these significant advantages, the need for expert supervision of artificial intelligence-generated results to ensure diagnostic accuracy and reliability is emphasized. Moreover, the review highlights the importance of adapting algorithms to different scanning protocols and population-specific features. Additionally, the importance of interdisciplinary collaboration among cardiologists, radiologists, data scientists, and software developers for the effective integration into routine clinical practice is pointed out. Therefore, the review outlines the potential of artificial intelligence technologies to enhance diagnostic quality and underscores the need for further clinical research and standardization of methods for successful integration into daily practice.

Ранняя диагностика аневризм грудного отдела аорты и патологического расширения лёгочного ствола имеет решающее значение для предотвращения серьёзных осложнений, включая разрыв сосудистой стенки и острую правожелудочковую недостаточность, а также для снижения смертности от сердечно-сосудистых заболеваний. В представленном обзоре рассматриваются современные подходы к визуализации этих патологий, с акцентом на использование компьютерной томографии в качестве «золотого стандарта». Отдельное внимание уделено внедрению технологий искусственного интеллекта, которые позволяют автоматически сегментировать сосудистые структуры, измерять их диаметр и проводить оппортунистический скрининг, выявляя скрытые патологии на ранних стадиях без необходимости проведения дополнительных исследований, что снижает нагрузку на врачей-рентгенологов и повышает качество медицинской помощи. Подробно анализируется опыт Московского эксперимента, в рамках которого использование технологий искусственного интеллекта в анализе медицинских изображений показало высокую чувствительность, воспроизводимость и сокращение времени описания. Несмотря на значительные преимущества, подчёркивается необходимость контроля результатов работы искусственного интеллекта специалистами для обеспечения точности и надёжности диагностики. Также отмечается актуальность адаптации алгоритмов к разным протоколам сканирования и популяционным особенностям. Кроме того, подчёркивается важность междисциплинарного взаимодействия кардиологов, рентгенологов, инженеров-данных и разработчиков программного обеспечения для для эффективного внедрения в рутинную клиническую деятельность. В заключение делается вывод о значительном потенциале технологий искусственного интеллекта для повышения качества диагностики и подчёркивается необходимость дальнейших клинических исследований и стандартизации методик для их успешной интеграции в повседневную практику.

胸主动脉瘤和肺动脉干异常扩张的早期诊断对于预防血管壁破裂、急性右心衰竭等严重并发症，以及降低心血管疾病相关死亡率具有关键意义。本文综述了当前针对上述病变的影像学诊断策略，重点强调将计算机断层扫描作为“金标准”的应用地位。文章特别关注人工智能技术在临床中的应用，这些技术能够自动分割血管结构、测量其直径，并执行机会性筛查，在无需进行额外检查的情况下识别早期潜在病变，从而减轻放射科医生的负担，提高医疗服务质量。文章详细分析了“Moscow Experiment”的经验。在该实验中，人工智能技术在医学影像分析中的应用表现出较高的灵敏度、良好的结果重复性，并显著缩短了报告撰写时间。尽管人工智能具备诸多优势，但仍强调需由专业人员对其结果进行把关，以确保诊断的准确性与可靠性。同时强调将算法适配于不同扫描协议和人群特征的必要性。此外，还需强调心血管病学专家、放射科医生、数据工程师与软件开发人员之间的跨学科合作对于将有效融入常规临床实践的重要性。综上所述，人工智能技术在提升诊断质量方面具有巨大潜力，同时强调进一步开展临床研究及标准化相关方法对于其成功融入日常实践的必要性。

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