Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector62319610.17816/DD623196Systematic reviewsСистематические обзоры系统评价Review ArticleUse of artificial intelligence in the diagnosis of arterial calcificationПрименение искусственного интеллекта в диагностике кальцификации артерий人工智能在动脉钙化诊断中的应用https://orcid.org/0000-0001-6407-38803203-5314TrusovYuri А.ТрусовЮрий АлександровичTrusovYuri А.
Russian Federation
yu.a.trusov@samsmu.ruhttps://orcid.org/0009-0003-8318-36734402-7476ChupakhinaVictoria S.ЧупахинаВиктория СергеевнаChupakhinaVictoria S.
Russian Federation
chupalhina@bk.ruhttps://orcid.org/0009-0006-8621-55803307-5546NurkaevaAdilya S.НуркаеваАдиля СалаватовнаNurkaevaAdilya S.
Russian Federation
vkomissiya@inbox.ruhttps://orcid.org/0009-0005-6726-96234415-2236YakovenkoNatalia A.ЯковенкоНаталья АлександровнаYakovenkoNatalia A.
Russian Federation
tigris2011@yandex.ruhttps://orcid.org/0009-0006-6222-93394123-3336AbleninaIrina V.АбленинаИрина ВитальевнаAbleninaIrina V.
Russian Federation
aninelba@gmail.comhttps://orcid.org/0009-0004-5057-64513542-3376LatypovaRoksana F.ЛатыповаРоксана ФанилевнаLatypovaRoksana F.
Russian Federation
roxevansss@gmail.comhttps://orcid.org/0009-0002-1111-759X3726-4213PitkeAleksandra P.ПиткеАлександра ПетровнаPitkeAleksandra P.
Russian Federation
pitkea00@gmail.comhttps://orcid.org/0000-0002-3955-08303543-5323YazovskihAnastasiya A.ЯзовскихАнастасия АлексеевнаYazovskihAnastasiya A.
Russian Federation
anyaz.bgmu@yandex.ruhttps://orcid.org/0009-0000-3562-82934834-5324IvanovArtem S.ИвановАртем СергеевичIvanovArtem S.
Russian Federation
artem.ivanov656@yandex.ruhttps://orcid.org/0009-0004-5055-88193331-3421BogatyrevaDarya S.БогатыреваДарья СергеевнаBogatyrevaDarya S.
Russian Federation
diria1012@yandex.ruhttps://orcid.org/0009-0002-7994-56313452-2543PopovaUlyana A.ПоповаУльяна АндреевнаPopovaUlyana A.
Russian Federation
ulyanka.popova.2000@gmail.comhttps://orcid.org/0009-0002-8799-47324812-3213YuzlekbaevAzat F.ЮзлекбаевАзат ФлюровичYuzlekbaevAzat F.
Russian Federation
ztl5@rambler.ruSamara State Medical UniversityСамарский государственный медицинский университетSamara State Medical UniversityRostov State Medical UniversityРостовский государственный медицинский университетRostov State Medical UniversityBashkir State Medical UniversityБашкирский государственный медицинский университетBashkir State Medical UniversitySechenov First Moscow State Medical UniversityПервый Московский государственный медицинский университет им. И.М. СеченоваSechenov First Moscow State Medical UniversityOrenburg State Medical UniversityОренбургский государственный медицинский университетOrenburg State Medical UniversityRussian National Research Medical University named after N.I. PirogovРоссийский национальный исследовательский медицинский университет им. Н.И. ПироговаRussian National Research Medical University named after N.I. PirogovRussian University of MedicineРоссийский университет медициныRussian University of Medicine290120241904202451851001011202310012024Copyright ©; 2024, Eco-VectorCopyright ©; 2024, Эко-векторCopyright ©; 2024, Eco-Vector2024Eco-VectorЭко-векторEco-Vectorhttps://creativecommons.org/licenses/by-nc-nd/4.0/https://jdigitaldiagnostics.com/DD/article/view/623196

BACKGROUND: The incidence of circulatory system diseases in the Russian Federation has been steadily increasing during the last two decades, growing 2,047 times between 2000 and 2019. Vascular calcification involves the deposition of calcium salts in the artery wall, which leads to vascular wall remodeling. X-ray imaging is the gold standard for diagnosing of vascular calcification. However, because of the need to process an increasing amount of data in a shorter period of time, the number of diagnostic errors inevitably increases, and work efficiency inevitably decreases. The active development and introduction of artificial intelligence into clinical practice have created opportunities for specialists to address these issues.

AIM: To analyze the national and international literature on the use of artificial intelligence in the diagnosis of various vascular calcifications, summarize the prognostic value of vascular calcification, and evaluate aspects that prevent the diagnosis of vascular calcification without using artificial intelligence.

MATERIALS AND METHODS: A search was performed in PubMed, Web of Science, Google Scholar, and eLibrary. The search was conducted using the following keywords: artificial intelligence, machine learning, vascular calcification, and their analogues in Russian. The search covered the period from inception till July 2023.

RESULTS: The studies included in the review compared the diagnostic abilities of clinicians and artificial intelligence using the same images, with subsequent assessment of the accuracy, speed, and other parameters. The sites of vascular calcification varied, resulting in differences in their prognostic value.

CONCLUSION: Artificial intelligence has proven to be effective in the diagnosis of vascular calcification. In addition to improved accuracy and efficiency, the level of detail is superior to manual diagnosis methods. Artificial intelligence has advanced to the point that imaging specialists can automatically detect vascular calcification. Artificial intelligence can contribute to the successful development of X-ray imaging in the future.

Обоснование. Показатели заболеваемости населения Российской Федерации патологиями системы кровообращения за прошедшие два десятилетия постоянно повышались, и с 2000 г. до 2019 г. увеличились в 2,047 раза. Процесс кальцификации сосудов включает отложение солей кальция в стенке артерий, что приводит к ремоделированию сосудистой стенки. Лучевые методы исследования — золотой стандарт диагностики кальцификации сосудов. Однако в связи с возрастающим объёмом данных и необходимостью сокращения времени постановки диагноза неизбежно снижается эффективность работы. Активное развитие и внедрение в клиническую практику искусственного интеллекта открыло перед специалистами возможности для решения этих проблем.

Цель — проанализировать отечественную и зарубежную литературу, посвящённую использованию искусственного интеллекта в диагностике различных типов кальцификации сосудов, а также обобщить прогностическую ценность кальцификации сосудов и оценить аспекты, препятствующие диагностике кальцификации сосудов без применения искусственного интеллекта.

Материалы и методы. Авторы провели поиск публикаций в электронных базах данных PubMed, Web of Science, Google Scholar и eLibrary. Поиск проводился по следующим ключевым словам: «artificial intelligence», «machine learning», «vascular calcification», «искусственный интеллект», «машинное обучение», «кальцификация сосудов». Поиск проводился во временном интервале с момента основания соответствующей базы данных до июля 2023 года.

Результаты. Основная методология включённых в обзор исследований заключалась в сравнении диагностических способностей клиницистов и искусственного интеллекта с применением одних и тех же изображений и последующей оценкой точности, скорости и других показателей. Участки возникновения сосудистых кальцификаций весьма разно-образны, что обусловливает их различную прогностическую ценность.

Заключение. Искусственный интеллект отлично зарекомендовал себя в диагностике сосудистой кальцификации. Помимо повышения точности и эффективности, способности к детализации превосходят возможности ручного метода диагностики. Искусственный интеллект достиг уровня, позволяющего помогать врачам инструментальной диагностики в автоматическом выявлении кальцификации сосудов. Возможности искусственного интеллекта могут способствовать эффективному развитию рентгенологии в будущем.

论证。近二十年来，俄罗斯联邦居民循环系统疾病的发病率持续上升。从2000年到2019年，此类疾病的数量增加了2.047倍。血管钙化过程包括钙盐在动脉壁的沉积，这导致血管壁重塑。放射性检查方法是诊断血管钙化的金标准。然而，随着数据量的增加和诊断时间的需要，工作效率不可避免地下降，人工智能的积极发展和应用于临床为专家解决这些问题提供了机会。

目的。本研究的目的是分析国内外关于使用人工智能诊断不同类型血管钙化的文献，同时，总结血管钙化的预后价值，并评估在不使用人工智能的情况下阻碍血管钙化诊断的方面。

材料与方法。在电子数据库PubMed、Web of Science、Google Scholar和eLibrary中搜索了相关出版物。搜索时使用了以下关键词：“artificial intelligence”，“machine learning”，“vascular calcification”，“人工智能”、“机器学习”、“血管钙化”。检索时间为相关数据库建立至2023年7月。

结果。综述中包含的研究的主要方法是比较临床医生和人工智能使用相同图片的诊断能力，然后评估准确性、速度和其他指标。血管钙化发生的部位差异很大，这也是其预后价值不同的原因。

结论。事实证明，人工智能在诊断血管钙化方面表现出色。除了提高准确性和效率外，其细节处理能力也超过人工诊断方法。人工智能已经达到了帮助仪器诊断医生自动检测血管钙化的水平。未来，人工智能的能力可以促进放射学的有效发展。

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