现代人工智能技术在心血管影像学中的应用前景

封面图片

如何引用文章

全文:

详细

心血管疾病是全球致残和死亡的主要原因。新技术的出现以及人工智能和机器学习的引入为医生提供了提高诊断和治疗效率的机会。人工智能技术，尤其是在机器学习和深度学习领域的迅猛发展，迅速吸引了临床医生的关注，推动他们创建新的集成化、可靠和高效的诊断方法，以提供医疗帮助。心脏病学专家使用广泛的基于影像学的诊断方法，相比其他许多专业领域，他们能够获得更为广泛的患者定量信息。通过本综述，我们试图总结现有文献中关于人工智能技术在心血管疾病诊断中的应用，同时识别需要进一步研究的知识空白。心脏病学是医学领域中机器学习和深度学习方法得到广泛应用并显示出有前景成果的领域之一。卷积神经网络成功用于通过超声心动图测量心脏功能参数。深度学习算法有助于更准确地识别冠状动脉的狭窄和钙化，以及通过心脏计算机断层扫描数据确定斑块特征。卷积神经网络还用于自动分割心脏腔室和结构、确定组织特性以及通过磁共振成像进行灌注分析等任务。随着人工智能技术，尤其是机器学习技术的发展，其集成将带来新的可能性。因此，人工智能技术在医疗卫生领域具有广泛的兴趣，因为它们能够在短时间内分析大量信息，展示出高度的效率。人工智能能够为专家提供额外支持，从而提高工作效率和医疗服务的质量。

作者简介

Almaz Kh. Islamgulov

Bashkir State Medical University

编辑信件的主要联系方式.
Email: aslmaz2000@rambler.ru
ORCID iD: 0000-0003-0567-7515
SPIN 代码: 8701-3486
俄罗斯联邦, Ufa

Alina S. Bogdanova

Kuban State Medical University

Email: balinochka25@gmail.com
ORCID iD: 0009-0004-9333-5164
俄罗斯联邦, Krasnodar

Damir I. Sufiiarov

Bashkir State Medical University

Email: damur_5@mail.ru
ORCID iD: 0009-0004-3516-6307
SPIN 代码: 3311-2947
俄罗斯联邦, Ufa

Alina V. Chernyavskaya

Kuban State Medical University

Email: alinaxxx909@gmail.com
ORCID iD: 0009-0007-8071-1150
俄罗斯联邦, Krasnodar

Elena R. Bairakaeva

Bashkir State Medical University

Email: bairakaeva_0@mail.ru
ORCID iD: 0009-0004-7683-5781
俄罗斯联邦, Ufa

Anastasia A. Maksimova

Bashkir State Medical University

Email: antasiamks@gmail.com
ORCID iD: 0009-0003-4115-2887
俄罗斯联邦, Ufa

Nikita V. Nemychnikov

Bashkir State Medical University

Email: nikita.nemychnikov2001@gmail.com
ORCID iD: 0009-0001-8841-3373
俄罗斯联邦, Ufa

Diana R. Bikieva

Bashkir State Medical University

Email: bikieva.dina@mail.ru
ORCID iD: 0009-0006-5453-5686
SPIN 代码: 7078-7424
俄罗斯联邦, Ufa

Alsu I. Shakhmaeva

Bashkir State Medical University

Email: shakhmaeva02@mail.ru
ORCID iD: 0009-0002-8805-9172
俄罗斯联邦, Ufa

Lyubov A. Burdina

Pskov State University

Email: lubovburdina19@gmail.com
ORCID iD: 0009-0004-9199-2515
俄罗斯联邦, Pskov

Aleksandr V. Bolekhan

Pskov State University

Email: sasha-x500@mail.ru
ORCID iD: 0009-0009-3458-2858
俄罗斯联邦, Pskov

Egor I. Akimov

Tula State University

Email: egor.akimov.2001@mail.ru
ORCID iD: 0009-0002-2504-5363
俄罗斯联邦, Tula

Zilya Z. Shurakova

Bashkir State Medical University

Email: divaeva.zilya@mail.ru
ORCID iD: 0009-0007-9625-9787
俄罗斯联邦, Ufa

参考

  1. Kosolapov VP, Yarmonova MV. The analysis of high cardiovascular morbidity and mortality in the adult population as a medical and social problem and the search for ways to solve it. Ural Medical Journal. 2021;20(1):58–64. doi: 10.52420/2071-5943-2021-20-1-58-64 EDN: HCWKUA
  2. Yeo KK. Artificial intelligence in cardiology: did it take off? Russian Journal for Personalized Medicine. 2023;2(6):16–22. doi: 10.18705/2782-3806-2022-2-6-16-22 EDN: UIENOT
  3. Xu B, Kocyigit D, Grimm R, et al. Applications of artificial intelligence in multimodality cardiovascular imaging: a state-of-the-art review. Progress in Cardiovascular Diseases. 2020;63(3):367–376. doi: 10.1016/j.pcad.2020.03.003
  4. Turing AM. I.–Computing machinery and intelligence. Mind. 1950;LIX(236):433–460. doi: 10.1093/mind/LIX.236.433
  5. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 1955;27(4):12. doi: 10.1609/aimag.v27i4.1904
  6. Komkov AA, Mazaev VP, Ryazanova SV, et al. First study of the RuPatient health information system with optical character recognition of medical records based on machine learning. Cardiovascular Therapy and Prevention. 2022;20(8):91–96. doi: 10.15829/1728-8800-2021-3080 EDN: VOUGRB
  7. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. doi: 10.1038/nature14539
  8. Gritskov IO, Govorov AV, Vasiliev AO, et al. Data Science – deep learning of neural networks and their application in healthcare. City Healthcare. 2021;2(2):109–115. doi: 10.47619/2713-2617.zm.2021.v2i2;109-115 EDN: SGWBPD
  9. Vardas PE, Asselbergs FW, van Smeden M, Friedman P. The year in cardiovascular medicine 2021: digital health and innovation. Eur Heart J. 2022;43(4):271–279. doi: 10.1093/eurheartj/ehab874 EDN: CCJAGO
  10. Maltseva AN, Kop’eva KV, Mochula AV, et al. Association of impaired myocardial flow reserve with risk factors for cardiovascular diseases in patients with nonobstructive coronary artery disease. Russian Journal of Cardiology. 2023;28(2):50–59. (In Russ.) doi: 10.15829/1560-4071-2023-5158 EDN: FNSYNE
  11. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7 EDN: OQSRZW
  12. Donahue J, Hendricks LA, Rohrbach M, et al. Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):677–691. doi: 10.1109/TPAMI.2016.2599174
  13. Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evol Intell. 2022;15(1):1–22. doi: 10.1007/s12065-020-00540-3 EDN: GOCYDD
  14. Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021;12(1):1–9. doi: 10.1038/s41467-021-20966-2 EDN: KYJQRH
  15. Amini M, Pursamimi M, Hajianfar G, et al. Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: a radiomics study. Sci Rep. 2023;13(1):14920. doi: 10.1038/s41598-023-42142-w EDN: HGXHIT
  16. Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092–1101. doi: 10.1161/CIRCRESAHA.117.311312
  17. Ntalianis E, Cauwenberghs N, Sabovčik F, et al. Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population. Front Cardiovasc Med. 2023;10:1263301. doi: 10.3389/fcvm.2023.1263301 EDN: VEPAAS
  18. Zhao J, Hou X, Pan M, Zhang H. Attention-based generative adversarial network in medical imaging: a narrative review. Comput Biol Med. 2022;149:105948. doi: 10.1016/j.compbiomed.2022.105948 EDN: TBRKVW
  19. Al'Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur Heart J. 2020;41(3):359–367. doi: 10.1093/eurheartj/ehz565 EDN: UYDWAD
  20. Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: a radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res. 2020;116(13):2040–2054. doi: 10.1093/cvr/cvaa021 EDN: JJYPCZ
  21. Fleg JL, Stone GW, Fayad ZA, et al. Detection of high-risk atherosclerotic plaque: report of the NHLBI Working Group on current status and future directions. JACC Cardiovasc Imaging. 2012;5(9):941–955. doi: 10.1016/j.jcmg.2012.07.007
  22. Chen Q, Zhou F, Xie G, et al. Advances in artificial intelligence-assisted coronary computed tomographic angiography for atherosclerotic plaque characterization. Rev Cardiovasc Med. 2024;25(1):27. doi: 10.31083/j.rcm2501027 EDN: OLVUVT
  23. Zvartau NE, Solovyova AE, Endubaeva GV, et al. Analysis of the information about the incidence of heart failure, associated mortality and burden on the healthcare system, based on the encoding data in 15 subjects of the Russian Federation. Russian Journal of Cardiology. 2023;28(2S):9–15. doi: 10.15829/1560-4071-2023-5339 EDN: YOUIRD
  24. Miller PK, Waring L, Bolton GC, Sloane C. Personnel flux and workplace anxiety: personal and interpersonal consequences of understaffing in UK ultrasound departments. Radiography (Lond). 2019;25(1):46–50. doi: 10.1016/j.radi.2018.07.005
  25. Gao XF, Ge Z, Kong XQ, et al. 3-Year Outcomes of the ULTIMATE Trial Comparing Intravascular Ultrasound Versus Angiography-Guided Drug-Eluting Stent Implantation. JACC Cardiovasc Interv. 2021;14(3):247–257. doi: 10.1016/j.jcin.2020.10.001 EDN: RXYWYL
  26. Osipova OA, Kontsevaya AV, Demko VV, et al. Elements of artificial intelligence in a predictive personalized model of pharmacotherapy choice in patients with heart failure with mildly reduced ejection fraction of ischemic origin. Cardiovascular Therapy and Prevention. 2023;22(7):16–24. doi: 10.15829/1728-8800-2023-3619 EDN: XLOMXO
  27. Luong CL, Jafari MH, Behnami D, et al. Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance. Echo Res Pract. 2024;11(1):9. doi: 10.1186/s44156-024-00043-2
  28. Olaisen S, Smistad E, Espeland T, et al. Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases. Eur Heart J Cardiovasc Imaging. 2024;25(3):383–395. doi: 10.1093/ehjci/jead280 EDN: ALCWDT
  29. Knackstedt C, Bekkers SC, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study. J Am Coll Cardiol. 2015;66(13):1456–1466. doi: 10.1016/j.jacc.2015.07.052
  30. Narula S, Shameer K, Salem Omar AM, et al. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287–2295. doi: 10.1016/j.jacc.2016.08.062
  31. Zhang J, Gajjala S, Agrawal P. Fully automated echocardiogram interpretation in clinical practice. Circulation. 2018;138(16):1623–1635. doi: 10.1161/CIRCULATIONAHA.118.034338
  32. Sehly A, Jaltotage B, He A. Artificial intelligence in echocardiography: the time is now. Rev Cardiovasc Med. 2022;23(8):256. doi: 10.31083/j.rcm2308256 EDN: LTPRNG
  33. Playford D, Bordin E, Mohamad R, et al. Enhanced diagnosis of severe aortic stenosis using artificial intelligence: a proof-of-concept study of 530,871 echocardiograms. JACC Cardiovasc Imaging. 2020;13(4):1087–1090. doi: 10.1016/j.jcmg.2019.10.013 EDN: QKVLDF
  34. Zhang Y, Wang M, Zhang E, Wu Y. Artificial intelligence in the screening, diagnosis, and management of aortic stenosis. Rev Cardiovasc Med. 2024;25(1):31. doi: 10.31083/j.rcm2501031 EDN: MGUQSK
  35. Ouyang D, He B, Ghorbani A. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580(7802):252–256. doi: 10.1038/s41586-020-2145-8
  36. Tereshchenko SN, Zhirov IV, Uskach TM, et al. Eurasian association of cardiology (EAC)/ The National society of heart failure and myocardial disease (NSHFMD) Guidelines for the diagnosis and and treatment of chronic heart failure (2020). Eurasian heart journal. 2020;(3):6–76. doi: 10.38109/2225-1685-2020-3-6-76 EDN: WPQNAB
  37. Bustin A, Fuin N, Botnar RM, Prieto C. From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction. Front Cardiovasc Med. 2020;7:17. doi: 10.3389/fcvm.2020.00017
  38. Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20(1):1–12. doi: 10.1186/s12968-018-0471-x EDN: XCDICM
  39. Bhuva AN, Bai W, Lau C. A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis. Circ Cardiovasc Imaging. 2019;12(10):e009214. doi: 10.1161/CIRCIMAGING.119.009214
  40. Celant LR, Wessels JN, Marcus JT. Toward the implementation of optimal cardiac magnetic resonance risk stratification in pulmonary arterial hypertension. Chest. 2024;165(1):181–191. doi: 10.1016/j.chest.2023.07.028 EDN: KJHFYB
  41. Farrag NA, Lochbihler A, White JA, Ukwatta E. Evaluation of fully automated myocardial segmentation techniques in native and contrast-enhanced T1-mapping cardiovascular magnetic resonance images using fully convolutional neural networks. Med Phys. 2021;48(1):215–226. doi: 10.1002/mp.14574
  42. Bernard O, Lalande A, Zotti C. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging. 2018;37(11):2514–2525. doi: 10.1109/TMI.2018.2837502
  43. Fahmy AS, Rausch J, Neisius U. Automated cardiac MR scar quantification in hypertrophic cardiomyopathy using deep convolutional neural networks. JACC Cardiovasc Imaging. 2018;11(12):1917–1918. doi: 10.1016/j.jcmg.2018.04.030
  44. Fahmy AS, Neisius U, Chan RH, et al. Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study. Radiology. 2020;294(1):52–60. doi: 10.1148/radiol.2019190737
  45. Chen C, Bai W, Davies RH, et al. Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front Cardiovasc Med. 2020;7:105. doi: 10.3389/fcvm.2020.00105
  46. Sinitsyn VE, Mershina EA, Larina OM. Cardiac magnetic resonance imaging opportunities in the diagnosis of cardiomyopathy. Clinical and Experimental Surgery. Petrovsky journal. 2014;(1):54–63. EDN: SDUECR
  47. Khludova LG. Hypersensitivity reactions to contrast media. Astma i allergiya. 2019;(2):8–11. (In Russ.) EDN: GVMUZB
  48. Zhang Q, Burrage MK, Lukaschuk E, et al. Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy. Circulation. 2021;144(8):589–599. doi: 10.1161/CIRCULATIONAHA.121.054432 EDN: BJCVTF
  49. Leiner T, Rueckert D, Suinesiaputra A, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019;21(1):1–14. doi: 10.1186/s12968-019-0575-y EDN: UTXASC
  50. Knott KD, Seraphim A, Augusto JB, et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping. Circulation. 2020;141(16):1282–1291. doi: 10.1161/CIRCULATIONAHA.119.044666 EDN: HORRTM
  51. Shesternikova OP, Finn VK, Lesko KA, Vinokurova LV. Intelligent system for predicting the feasibility of using computed tomography. Artificial Intelligence and Decision Making. 2022;(2):3–16. doi: 10.14357/20718594220201 EDN: QSUQRY
  52. Krittanawong C, Virk HUH, Bangalore S, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep. 2020;10(1):16057. doi: 10.1038/s41598-020-72685-1 EDN: TUAGSP
  53. Abdulalimov TP, Obrezan AG. Artificial intelligence capabilities in predicting coronary artery disease. Cardiology: News, Opinions, Training. 2022;10(1):34–39. doi: 10.33029/2309-1908-2022-10-1-34-39 EDN: JRHPMV
  54. van Rosendael AR, Maliakal G, Kolli KK, et al. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J Cardiovasc Comput Tomogr. 2018;12(3):204–209. doi: 10.1016/j.jcct.2018.04.011
  55. Han D, Kolli KK, Gransar H, et al. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: comparison with traditional risk prediction approaches. J Cardiovasc Comput Tomogr. 2020;14(2):168–176. doi: 10.1016/j.jcct.2019.09.005
  56. Motwani M, Dey D, Berman DS, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–507. doi: 10.1093/eurheartj/ehw188
  57. Klüner LV, Chan K, Antoniades C. Using artificial intelligence to study atherosclerosis from computed tomography imaging: a state-of-the-art review of the current literature. Atherosclerosis. 2024;398:117580. doi: 10.1016/j.atherosclerosis.2024.117580 EDN: BGKJLP
  58. Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392(10151):929–939. doi: 10.1016/S0140-6736(18)31114-0 EDN: CFUNJT
  59. Wolterink JM, Leiner T, de Vos BD, et al. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123–136. doi: 10.1016/j.media.2016.04.004
  60. Zreik M, Lessmann N, van Hamersvelt RW, et al. Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis. Med Image Anal. 2018;44:72–85. doi: 10.1016/j.media.2017.11.008
  61. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol. 2019;29(5):2350–2359. doi: 10.1007/s00330-018-5822-3 EDN: WVYVWW
  62. Kelm BM, Mittal S, Zheng Y, et al. Detection, grading and classification of coronary stenoses in computed tomography angiography. Med Image Comput Comput Assist Interv. 2011;14(Pt 3):25–32. doi: 10.1007/978-3-642-23626-6_4
  63. Zreik M, van Hamersvelt RW, Wolterink JM, et al. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging. 2019;38(7):1588–1598. doi: 10.1109/TMI.2018.2883807
  64. Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board. Radiology. 2020;294(3):487–489. doi: 10.1148/radiol.2019192515
  65. Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proc ACM Conf Health Inference Learn (CHIL 2020). Association for Computing Machinery. New York, 2020. P. 151–159. doi: 10.1145/3368555.3384468
  66. Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans Neural Netw Learn Syst. 2021;32(11):4793–4813. doi: 10.1109/TNNLS.2020.3027314 EDN: BZXVNY
  67. DeGrave AJ, Janizek JD, Lee SI. AI for radiographic COVID-19 detection selects shortcuts over signal. Nature Machine Intelligence. 2021;3(7):610–619. doi: 10.1038/s42256-021-00338-7 EDN: MMHUHL

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1

下载 (213KB)
索引源数据

版权所有 © Eco-Vector, 2025

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».