Use of artificial intelligence in the diagnosis of arterial calcification

Cover Page

Cite item

Full Text

Abstract

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.

About the authors

Yuri А. Trusov

Samara State Medical University

Email: yu.a.trusov@samsmu.ru
ORCID iD: 0000-0001-6407-3880
SPIN-code: 3203-5314
Russian Federation, Samara

Victoria S. Chupakhina

Rostov State Medical University

Email: chupalhina@bk.ru
ORCID iD: 0009-0003-8318-3673
SPIN-code: 4402-7476
Russian Federation, Rostov-on-Don

Adilya S. Nurkaeva

Bashkir State Medical University

Email: vkomissiya@inbox.ru
ORCID iD: 0009-0006-8621-5580
SPIN-code: 3307-5546
Russian Federation, Ufa

Natalia A. Yakovenko

Sechenov First Moscow State Medical University

Email: tigris2011@yandex.ru
ORCID iD: 0009-0005-6726-9623
SPIN-code: 4415-2236
Russian Federation, Moscow

Irina V. Ablenina

Orenburg State Medical University

Email: aninelba@gmail.com
ORCID iD: 0009-0006-6222-9339
SPIN-code: 4123-3336
Russian Federation, Orenburg

Roksana F. Latypova

Orenburg State Medical University

Email: roxevansss@gmail.com
ORCID iD: 0009-0004-5057-6451
SPIN-code: 3542-3376
Russian Federation, Orenburg

Aleksandra P. Pitke

Orenburg State Medical University

Email: pitkea00@gmail.com
ORCID iD: 0009-0002-1111-759X
SPIN-code: 3726-4213
Russian Federation, Orenburg

Anastasiya A. Yazovskih

Bashkir State Medical University

Email: anyaz.bgmu@yandex.ru
ORCID iD: 0000-0002-3955-0830
SPIN-code: 3543-5323
Russian Federation, Ufa

Artem S. Ivanov

Bashkir State Medical University

Email: artem.ivanov656@yandex.ru
ORCID iD: 0009-0000-3562-8293
SPIN-code: 4834-5324
Russian Federation, Ufa

Darya S. Bogatyreva

Russian National Research Medical University named after N.I. Pirogov

Email: diria1012@yandex.ru
ORCID iD: 0009-0004-5055-8819
SPIN-code: 3331-3421
Russian Federation, Moscow

Ulyana A. Popova

Russian University of Medicine

Email: ulyanka.popova.2000@gmail.com
ORCID iD: 0009-0002-7994-5631
SPIN-code: 3452-2543
Russian Federation, Moscow

Azat F. Yuzlekbaev

Bashkir State Medical University

Author for correspondence.
Email: ztl5@rambler.ru
ORCID iD: 0009-0002-8799-4732
SPIN-code: 4812-3213
Russian Federation, Ufa

References

  1. Sharapova OV, Kicha DI, Gerasimova LI, et al. Map analysis of morbidity and mortality from blood circulatory system diseases of the population of the Russian federation (2010-2019). Complex Issues of Cardiovascular Diseases. 2022;11(1):56–68. EDN: ZUQVNA doi: 10.17802/2306-1278-2022-11-1-56-68
  2. Mal’kov OA, Govorukhina AA, Burykin YuG, Afineevskaya AYu. The Role of Calcification in the Pathogenesis of Inflammatory Reaction in the Arterial Wall (Exemplified by the Vessels of the Neck and Head in Adults). Journal of Medical and Biological Research. 2021;9(4):435–443. EDN: FTSKDS doi: 10.37482/2687-1491-Z081
  3. Archakova TV, Nedosugova LV. Factors of vascular calcification in patients with type 2 diabetes mellitus on long-term dialysis. Diabetes mellitus. 2020;23(2):125–131. EDN: KPXIVL doi: 10.14341/DM10145
  4. Mori H, Torii S, Kutyna M, et al. Coronary Artery Calcification and its Progression: What Does it Really Mean? JACC Cardiovasc Imaging. 2018;11(1):127–142. doi: 10.1016/j.jcmg.2017.10.012
  5. Yeo KK. Artificial intelligence in cardiology: did it take off? Russian Journal for Personalized Medicine. 2022;2(6):16–22. EDN: UIENOT doi: 10.18705/2782-3806-2022-2-6-16-22
  6. Karpov OE, Andrikov DA, Maksimenko VA, Hramov AE. Explainable artificial intelligence for medicine. Medical Doctor and IT. 2022;(2):4–11. EDN: DTCAWX doi: 10.25881/18110193_2022_2_4
  7. Greenland P, LaBree L, Azen SP, et al. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291(2):210–215. doi: 10.1001/jama.291.2.210
  8. Criqui MH, Denenberg JO, Ix JH, et al. Calcium density of coronary artery plaque and risk of incident cardiovascular events. JAMA. 2014;311(3):271–278. doi: 10.1001/jama.2013.282535
  9. Carr JJ, Jacobs DR Jr, Terry JG, et al. Association of Coronary Artery Calcium in Adults Aged 32 to 46 Years With Incident Coronary Heart Disease and Death. JAMA Cardiol. 2017;2(4):391–399. doi: 10.1001/jamacardio.2016.5493
  10. Khalikov AA, Kuznetsov KO, Iskuzhina LR, Khalikova LV. Forensic aspects of sudden autopsy-negative cardiac death. Sudebno-Meditsinskaya Ekspertisa. 2021;64(3):59–63. doi: 10.17116/sudmed20216403159
  11. Eisen A, Tenenbaum A, Koren-Morag N, et al. Calcification of the thoracic aorta as detected by spiral computed tomography among stable angina pectoris patients: association with cardiovascular events and death. Circulation. 2008;118(13):1328–1334. doi: 10.1161/CIRCULATIONAHA.107.712141
  12. Itani Y, Watanabe S, Masuda Y. Relationship between aortic calcification and stroke in a mass screening program using a mobile helical computed tomography unit. Circ J. 2006;70(6):733–736. doi: 10.1253/circj.70.733
  13. Lee R, Matsutani N, Polimenakos AC, et al. Preoperative noncontrast chest computed tomography identifies potential aortic emboli. Ann Thorac Surg. 2007;84(1):38–42. doi: 10.1016/j.athoracsur.2007.03.025
  14. Zweig BM, Sheth M, Simpson S, Al-Mallah MH. Association of abdominal aortic calcium with coronary artery calcium and obstructive coronary artery disease: a pilot study. Int J Cardiovasc Imaging. 2012;28(2):399–404. doi: 10.1007/s10554-011-9818-1
  15. An C, Lee HJ, Lee HS, et al. CT-based abdominal aortic calcification score as a surrogate marker for predicting the presence of asymptomatic coronary artery disease. Eur Radiol. 2014;24(10):2491–2498. doi: 10.1007/s00330-014-3298-3
  16. Melnikov MV, Zelinskiy VA, Zhorina AS, Chuglova DA. An abdominal aortic calcification in peripheral arterial occlusive disease: risk factors and markers. Journal of atherosclerosis and dyslipidemias. 2014;(3):33–38. EDN: SISNRZ
  17. Bagger YZ, Tankó LB, Alexandersen P, et al. Radiographic measure of aorta calcification is a site-specific predictor of bone loss and fracture risk at the hip. J Intern Med. 2006;259(6):598–605. doi: 10.1111/j.1365-2796.2006.01640.x
  18. Szulc P, Blackwell T, Schousboe JT, et al. High hip fracture risk in men with severe aortic calcification: MrOS study. J Bone Miner Res. 2014;29(4):968–975. doi: 10.1002/jbmr.2085
  19. Lobanova NI, Chicherina EN, Malchikova SV, Maksimchuk-Kolobova NS. Fluid shear stress on the endothelium of the carotid artery wall and coronary artery calcinosis in patients with arterial hypertension. South Russian Journal of Therapeutic Practice. 2022;3(3):60–67. EDN: QWCOCH doi: 10.21886/2712-8156-2022-3-3-60-67
  20. Kim JT, Yoo SH, Kwon JH, et al. Subtyping of ischemic stroke based on vascular imaging: analysis of 1,167 acute, consecutive patients. J Clin Neurol. 2006;2(4):225–230. doi: 10.3988/jcn.2006.2.4.225
  21. Wong LK. Global burden of intracranial atherosclerosis. Int J Stroke. 2006;1(3):158–159. doi: 10.1111/j.1747-4949.2006.00045.x
  22. Kockelkoren R, De Vis JB, de Jong PA, et al. Intracranial Carotid Artery Calcification From Infancy to Old Age. J Am Coll Cardiol. 2018;72(5):582–584. doi: 10.1016/j.jacc.2018.05.021
  23. Huang Z, Xiao J, Xie Y, et al. The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography. Sci Rep. 2020;10(1):11532. doi: 10.1038/s41598-020-68378-4
  24. Bochkareva EV, Butina EK, Bayramkulova EKh, et al. Prevalence and Severity of Breast Arterial Calcification on Routine Mammography. Rational Pharmacotherapy in Cardiology. 2022;18(5):530–535. EDN: HUFTZE doi: 10.20996/1819-6446-2022-09-01
  25. Bochkareva EV, Butina EK, Bayramkulova NKh, Drapkina OM. Mammary artery calcinosis and diabetes mellitus: case report and brief literature review. Profilakticheskaya Meditsina. 2021;24(9):97–101. EDN: QPQDLT doi: 10.17116/profmed20212409197
  26. Bochkareva EV, Butina EK, Savin AS, et al. Breast arteries calcification: a potential surrogate marker for cerebrovascular disease. Profilakticheskaya Meditsina. 2020;23(5):164–169. EDN: IRHLDZ doi: 10.17116/profmed202023051164
  27. Bochkareva EV, Butina EK, Savin AS, Drapkina OM. Breast artery calcification and osteoporosis in postmenopausal woman: a case report and opinion on the problem. Cardiovascular Therapy and Prevention. 2020;19(4):2574. EDN: RTDDQG doi: 10.15829/1728-8800-2020-2574
  28. Wang J, Ding H, Bidgoli FA, et al. Detecting Cardiovascular Disease from Mammograms With Deep Learning. IEEE Trans Med Imaging. 2017;36(5):1172–1181. doi: 10.1109/TMI.2017.2655486
  29. Golubev NA, Ogryzko EV, Tyurina EM, Shelepova EA, Shelekhov PV. Features of the development of the radiation diagnostics service in the Russian Federation for 2014–2019. Current problems of health care and medical statistics. 2021;(2):356–376. EDN: EHSADW doi: 10.24412/2312-2935-2021-2-356-376
  30. Krupinski EA, Berbaum KS, Caldwell RT, et al. Long radiology workdays reduce detection and accommodation accuracy. J Am Coll Radiol. 2010;7(9):698–704. doi: 10.1016/j.jacr.2010.03.004
  31. Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE. Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol. 2013;201(3):611–617. doi: 10.2214/AJR.12.10375
  32. Nikolaev AE, Shapiev AN, Blokhin IA, et al. New approaches for assessing coronary changes in multi-layer spiral computed tomography. Russian Journal of Cardiology. 2019;(12):124–130. EDN: VHYAYK doi: 10.15829/1560-4071-2019-12-124-130
  33. Guo X, O’Neill WC, Vey B, et al. SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms. Med Phys. 2021;48(10):5851–5861. doi: 10.1002/mp.15017
  34. Yacoub B, Kabakus IM, Schoepf UJ, et al. Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT. Acad Radiol. 2022;29(2):108–117. doi: 10.1016/j.acra.2021.02.007
  35. Isgum I, Rutten A, Prokop M, van Ginneken B. Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. Med Phys. 2007;34(4):1450–1461. doi: 10.1118/1.2710548
  36. Kurkure U, Chittajallu DR, Brunner G, et al. A supervised classification-based method for coronary calcium detection in non-contrast CT. Int J Cardiovasc Imaging. 2010;26(7):817–828. doi: 10.1007/s10554-010-9607-2
  37. Brunner G, Chittajallu DR, Kurkure U, Kakadiaris IA. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data. Int J Cardiovasc Imaging. 2010;26(7):829–838. doi: 10.1007/s10554-010-9608-1
  38. Brunner G, Kurkure U, Chittajallu DR, et al. Toward unsupervised classification of calcified arterial lesions. Med Image Comput Comput Assist Interv. 2008;11(1):144-152. doi: 10.1007/978-3-540-85988-8_18
  39. Takx RA, de Jong PA, Leiner T, et al. Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability. PLoS One. 2014;9(3):e91239. doi: 10.1371/journal.pone.0091239
  40. Wolterink JM, Leiner T, Takx RA, et al. Automatic Coronary Calcium Scoring in Non-Contrast-Enhanced ECG-Triggered Cardiac CT With Ambiguity Detection. IEEE Trans Med Imaging. 2015;34(9):1867–1878. doi: 10.1109/TMI.2015.2412651
  41. Saur SC, Alkadhi H, Desbiolles L, et al. Automatic detection of calcified coronary plaques in computed tomography data sets. Med Image Comput Comput Assist Interv. 2008;11(1):170–177. doi: 10.1007/978-3-540-85988-8_21
  42. Yang G, Chen Y, Ning X, et al. Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys. 2016;43(5):2174. doi: 10.1118/1.4945045
  43. Jiang B, Guo N, Ge Y, et al. Development and application of artificial intelligence in cardiac imaging. Br J Radiol. 2020;93(1113):20190812. doi: 10.1259/bjr.20190812
  44. Shahzad R, van Walsum T, Schaap M, et al. Vessel specific coronary artery calcium scoring: an automatic system. Acad Radiol. 2013;20(1):1–9. doi: 10.1016/j.acra.2012.07.018
  45. Cano-Espinosa C, González G, Washko GR, et al. Automated Agatston Score Computation in non-ECG Gated CT Scans Using Deep Learning. Proc SPIE Int Soc Opt Eng. 2018;10574:105742K. doi: 10.1117/12.2293681
  46. Gogin N, Viti M, Nicodème L, et al. Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning. Diagn Interv Imaging. 2021;102(11):683–690. doi: 10.1016/j.diii.2021.05.004
  47. 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
  48. Wang W, Wang H, Chen Q, et al. Coronary artery calcium score quantification using a deep-learning algorithm. Clin Radiol. 2020;75(3):237.e11–237.e16. doi: 10.1016/j.crad.2019.10.012
  49. Singh G, Al’Aref SJ, Lee BC, et al. End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning. Diagnostics (Basel). 2021;11(2):215. doi: 10.3390/diagnostics11020215
  50. Martin SS, van Assen M, Rapaka S, et al. Evaluation of a Deep Learning-Based Automated CT Coronary Artery Calcium Scoring Algorithm. JACC Cardiovasc Imaging. 2020;13(1):524–526. doi: 10.1016/j.jcmg.2019.09.015
  51. Zhang N, Yang G, Zhang W, et al. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. Eur J Radiol. 2021;134:109420. doi: 10.1016/j.ejrad.2020.109420
  52. de Vos BD, Wolterink JM, Leiner T, et al. Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT. IEEE Trans Med Imaging. 2019;38(9):2127–2138. doi: 10.1109/TMI.2019.2899534
  53. AlGhamdi M, Abdel-Mottaleb M, Collado-Mesa F. DU-Net: Convolutional Network for the Detection of Arterial Calcifications in Mammograms. IEEE Trans Med Imaging. 2020;39(10):3240–3249. doi: 10.1109/TMI.2020.2989737
  54. Nikolaev AE, Korkunova OA, Khutornoy IV, et al. Comparability of coronary risk assessment methods with chest ultra-LDCT and CT coronography with ECG synchronization. Medical Visualization. 2021;25(4):75–92. EDN: CMSGAX doi: 10.24835/1607-0763-1047
  55. van Assen M, Martin SS, Varga-Szemes A, et al. Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study. Eur J Radiol. 2021;134:109428. doi: 10.1016/j.ejrad.2020.109428
  56. Nikolaev AE, Shapiev AN, Blokhin IA. Standardization of coronary artery calcification assessment on contrast-free computed tomograms without ECG synchronization. Radiology Study. 2020;3(2):45–52. EDN: VVGXBI
  57. Isgum I, Prokop M, Niemeijer M, et al. Automatic coronary calcium scoring in low-dose chest computed tomography. IEEE Trans Med Imaging. 2012;31(12):2322–2334. doi: 10.1109/TMI.2012.2216889
  58. Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE Trans Med Imaging. 2017;36(12):2536–2545. doi: 10.1109/TMI.2017.2708987
  59. Klug M, Shemesh J, Green M, et al. A deep-learning method for the denoising of ultra-low dose chest CT in coronary artery calcium score evaluation. Clin Radiol. 2022;77(7):509–517. doi: 10.1016/j.crad.2022.03.005
  60. Sun Z, Ng CKC. Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study. Diagnostics (Basel). 2022;12(4):991. doi: 10.3390/diagnostics12040991
  61. Eng D, Chute C, Khandwala N, et al. Automated coronary calcium scoring using deep learning with multicenter external validation. NPJ Digit Med. 2021;4(1):88. doi: 10.1038/s41746-021-00460-1
  62. Morozov SP, Kokina DYu, Pavlov NA, et al. Clinical aspects of using artificial intelligence for the interpretation of chest X-rays. Tuberculosis and Lung Diseases. 2021;99(4):58–64. doi: 10.21292/2075-1230-2021-99-4-58-64
  63. Kamel PI, Yi PH, Sair HI, Lin CT. Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning. Radiol Cardiothorac Imaging. 2021;3(3):e200486. doi: 10.1148/ryct.2021200486
  64. Du T, Xie L, Zhang H, et al. Training and validation of a deep learning architecture for the automatic analysis of coronary angiography. EuroIntervention. 2021;17(1):32–40. doi: 10.4244/EIJ-D-20-00570
  65. Isgum I, Rutten A, Prokop M, et al. Automated aortic calcium scoring on low-dose chest computed tomography. Med Phys. 2010;37(2):714–723. doi: 10.1118/1.3284211
  66. de Vos BD, Lessmann N, de Jong PA, Išgum I. Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT. Radiol Cardiothorac Imaging. 2021;3(2):e190219. doi: 10.1148/ryct.2021190219
  67. van Velzen SGM, Lessmann N, Velthuis BK, et al. Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols. Radiology. 2020;295(1):66–79. doi: 10.1148/radiol.2020191621
  68. Guilenea FN, Casciaro ME, Pascaner AF, et al. Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients. Tomography. 2021;7(4):636–649. doi: 10.3390/tomography7040054
  69. Reid S, Schousboe JT, Kimelman D, et al. Machine learning for automated abdominal aortic calcification scoring of DXA vertebral fracture assessment images: A pilot study. Bone. 2021;148:115943. doi: 10.1016/j.bone.2021.115943
  70. Graffy PM, Liu J, O’Connor S, et al. Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort. Abdom Radiol (NY). 2019;44(8):2921–2928. doi: 10.1007/s00261-019-02014-2
  71. van Engelen A, Niessen WJ, Klein S, et al. Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty. PLoS One. 2014;9(4):e94840. doi: 10.1371/journal.pone.0094840
  72. Onishchenko PS, Klyshnikov KYu, Ovcharenko EA. Artificial neural networks in cardiology: analysis of graphic data. Bulletin of Siberian Medicine. 2021;20(4):193–204. EDN: XVBERA doi: 10.20538/1682-0363-2021-4-193-204
  73. Bortsova G, Bos D, Dubost F, et al. Automated Segmentation and Volume Measurement of Intracranial Internal Carotid Artery Calcification at Noncontrast CT. Radiol Artif Intell. 2021;3(5):e200226. doi: 10.1148/ryai.2021200226
  74. Li D, Qiao H, Han Y, et al. Histological validation of simultaneous non-contrast angiography and intraplaque hemorrhage imaging (SNAP) for characterizing carotid intraplaque hemorrhage. Eur Radiol. 2021;31(5):3106–3115. doi: 10.1007/s00330-020-07352-0
  75. Chen S, Ning J, Zhao X, et al. Fast simultaneous noncontrast angiography and intraplaque hemorrhage (fSNAP) sequence for carotid artery imaging. Magn Reson Med. 2017;77(2):753–758. doi: 10.1002/mrm.26111
  76. Qi H, Sun J, Qiao H, et al. Carotid Intraplaque Hemorrhage Imaging with Quantitative Vessel Wall T1 Mapping: Technical Development and Initial Experience. Radiology. 2018;287(1):276–284. doi: 10.1148/radiol.2017170526
  77. Cheng JZ, Cole EB, Pisano ED, Shen D. Detection of arterial calcification in mammograms by random walks. Inf Process Med Imaging. 2009;21:713–724. doi: 10.1007/978-3-642-02498-6_59
  78. Lomakov SYu. Volumes of mammographic studies in modern conditions of providing preventive measures. Profilakticheskaya Meditsina. 2020;23(4):41–44. EDN: OUUINQ doi: 10.17116/profmed20202304141
  79. Roseman DA, Hwang SJ, Manders ES, et al. Renal artery calcium, cardiovascular risk factors, and indexes of renal function. Am J Cardiol. 2014;113(1):156–161. doi: 10.1016/j.amjcard.2013.09.036

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Eco-Vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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».