Perm Medical JournalPerm Medical JournalПермский медицинский журнал (сетевое издание "Perm medical journal")0136-14492687-1408Eco-Vector67950810.17816/pmj42441-54Literature reviewОбзор литературыReview ArticleApplication of artificial intelligence in mathematical modeling of coronary blood flowВозможности применения искусственного интеллекта при математическом моделировании коронарного кровотокаhttps://orcid.org/0000-0003-3624-3226PorodikovA. A.ПородиковА. А.
Russian Federation

PhD (Medicine), Cardiovascular Surgeon

кандидат медицинских наук, сердечно-сосудистый хирург

faridun.azimov.98@list.ruhttps://orcid.org/0000-0002-9314-3558BiyanovA. N.БияновА. Н.
Russian Federation

PhD (Medicine), Pediatric Cardiologist

кандидат медицинских наук, детский кардиолог

faridun.azimov.98@list.ruhttps://orcid.org/0000-0002-1730-9050ArutyunyanV. B.АрутюнянВ. Б.
Russian Federation

PhD (Medicine), Cardiovascular Surgeon

кандидат медицинских наук, сердечно-сосудистый хирург

faridun.azimov.98@list.ruhttps://orcid.org/0009-0006-3286-6951AzimovF. F.АзимовФ. Ф.
Russian Federation

Medical Intern

врач-стажер

faridun.azimov.98@list.ruhttps://orcid.org/0000-0003-3867-648XBarulinaM. A.БарулинаМ. А.
Russian Federation

DSc (Physics and Mathematics), Director of the Institute of Physics and Mathematics

доктор физико-математических наук, директор Физико-математического института

faridun.azimov.98@list.ruhttps://orcid.org/0000-0003-3974-9011IvanovYa. N.ИвановЯ. Н.
Russian Federation

Master of Physics and Mathematics Institute

магистр Физико-математического института

faridun.azimov.98@list.ruS.G. Sukhanov Federal Center for Cardiovascular SurgeryФедеральный центр сердечно-сосудистой хирургии имени С.Г. СухановаPerm State National Research UniversityПермский государственный национальный исследовательский университет11092025424415410052025Copyright ©; 2025, Eco-VectorCopyright ©; 2025, Эко-Вектор2025Eco-VectorЭко-Векторhttps://permmedjournal.ru/PMJ/article/view/679508

Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide. In 2021 alone, there were more than 20 million deaths attributed to CVD, accounting for about a third of all deaths worldwide. An important factor influencing the mortality rate from cardiovascular diseases is the diagnostic and therapeutic strategies used to treat coronary heart disease. Investments in this area over the past 25 years have led to a reduction in the death rate from cardiovascular diseases in countries with a high socio-demographic index. Accurate diagnosis is the first step to choosing the appropriate treatment method.

The objective of the research is to study the literature data on the possibility of using artificial intelligence and mathematical modeling of medical research, in particular coronary angiography, for the analysis and development of computer programs for modeling cardiovascular and endovascular surgical interventions.

The search for Russian and foreign literature in Yandex and Google search engines, medical research websites PUB.MED was conducted using keywords: coronary angiography and artificial intelligence, mathematical modeling, fractional blood flow reserve, 3D modeling, coronary artery disease, percutaneous coronary intervention.

The practical application of AI to create mathematical models will allow reconstructing 3D pictures of coronary arteries, modeling blood flow, which significantly optimizes the treatment of coronary artery disease. This will make it possible to effectively plan endovascular interventions based on the patient's data in the absence of the patient himself. Further study of this issue promises great prospects for the development of mathematical modeling of coronary blood flow, making effective decisions during interventional procedures, which will reduce the incidence and mortality from cardiovascular diseases.

Сердечно-сосудистые заболевания (ССЗ) являются ведущей причиной смертности и инвалидности во всем мире. Только в 2021 г. на ССЗ пришлось более 20 млн летальных исходов, что составляет примерно треть всех смертей в мире. Важным фактором, влияющим на уровень смертности от сердечно-сосудистых заболеваний, являются диагностические и терапевтические стратегии, используемые для лечения ишемической болезни сердца. Инвестиции в эту область за последние 25 лет привели к снижению уровня смертности от сердечно-сосудистых заболеваний в странах с высоким социально-демографическим индексом. Точная диагностика является первым шагом к выбору подходящего метода лечения.

Изучены данные литературы о возможности применения искусственного интеллекта и математического моделирования медицинских исследований, в частности коронароангиографии, для анализа и создания компьютерных программ по моделированию сердечно-сосудистых и эндоваскулярных оперативных вмешательств.

Осуществлен поиск отечественной и зарубежной литературы в поисковиках «Яндекс» и Googl, PUB.MED по ключевым словам: «коронароангиография», «искусственный интеллект», «математическое моделирование», «фракционный резерв кровотока», «3D-моделирование», «ишемическая болезнь сердца», «чрескожное коронарное вмешательство».

Практическое применение ИИ для создания математических моделей позволит реконструировать 3D-картины коронарных артерий, моделирование кровотока, что значительно оптимизирует лечение ишемической болезни сердца. Благодаря этому удастся эффективно планировать эндоваскулярные вмешательства в отсутствии самого пациента по его данным. Дальнейшее изучение этого вопроса сулит огромные перспективы для развития математического моделирования коронарного кровотока, принятия эффективных решений при проведении интервенционных вмешательств, что позволит уменьшить заболеваемость и смертность от сердечно-сосудистых заболеваний.

Mathematical modelingcoronary angiographyfractional blood flow reserve. 3D modelingcoronary artery diseasepercutaneous coronary interventionматематическое моделированиекоронарная ангиографияфракционный резерв кровотока. 3D-моделированиеишемическая болезнь сердцачрескожное коронарное вмешательствоDornquast C., Kroll L.E., Neuhauser H.K., Willich S.N., Reinhold T., Busch M.A. Regional differences in the prevalence of cardiovascular disease. Dtsch Arztebl Int 2016; 113 (42): 704–11. DOI: 10.3238/arztebl.2016.0704 PMID: 27866565Mensah G.A., Roth G.A., Fuster V. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J Am College. Cardiol. 2019; 74 (20): 2529–2532. DOI: 10.1016/j.jacc.2019.10.009Roth G.A., Johnson C., Abajobir A. et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll. Cardiol. 2017; 70 (1): 1–25. DOI: 10.1016/j.jacc.2017.04.052Townsend N., Wilson L., Bhatnagar P., Wickramasinghe K., Rayner M., Nichols M. Cardiovascular disease in Europe: Epidemiological update 2016. Eur Heart J 2016; 37 (42): 3232–45. DOI: 10.1093/eurheartj/ehw334 PMID: 27523477Joseph P., Leong D., McKee M. et al. Reducing the global burden of cardiovascular disease, part 1: The epidemiology and risk factors. Circ Res 2017; 121 (6): 677–94. DOI: 10.1161/CIRCRESAHA. 117.308903 PMID: 28860318Bonaca M.P., Wiviott S.D., Braunwald E. et al. American college of cardiology/American heart association/European society of cardiology/world heart federation universal definition of myocardial infarction classification system and the risk of cardiovascular death: Observations from the triton-timi 38 trial (trial to assess improvement in therapeutic outcomes by optimizing platelet inhibition with prasugrel-thrombolysis in myocardial infarction 38). Circulation 2012; 125 (4): 577–83. DOI: 10.1161/CIRCULATIONAHA.111.041160 PMID: 22199016Di Carli M.F., Hachamovitch R. New technology for noninvasive evaluation of coronary artery disease. Circulation 2007; 115 (11): 1464–80. DOI: 10.1161/CIRCULATIONAHA.106.629808 PMID: 17372188Collet C., Onuma Y., Sonck J. et al. Diagnostic performance of angiography-derived fractional flow reserve: A systematic review and Bayesian meta-analysis. Eur Heart J 2018; 39 (35): 3314–21. DOI: 10.1093/eurheartj/ehy445 PMID: 30137305Collet C., Onuma Y., Sonck J. et al. Diagnostic performance of angiography-derived fractional flow reserve: A systematic review and Bayesian meta-analysis. Eur Heart J 2018; 39 (35): 3314–21. DOI: 10.1093/eurheartj/ehy445 PMID: 30137305Liu X., Wang Y., Zhang H. et al. Evaluation of fractional flow reserve in patients with stable angina: Can CT compete with angiography? Eur Radiol 2019; 29 (7): 3669–77. DOI: 10.1007/s00330-019-06023-z PMID: 30887203Ryan T.J. The coronary angiogram and its seminal contributions to cardiovascular medicine over five decades. Circulation 2002; 106 (6): 752–6. DOI: 10.1161/01.CIR.0000024109.12658.D4 PMID: 12163439Wang K.T., Chen C.Y., Chen Y.T. et al. Improving success rates of percutaneous coronary intervention for chronic total occlusion at arural Hospital in East Taiwan. Int J Gerontol 2014; 8 (3): 157–61. DOI: 10.1016/j.ijge.2013.12.004Sondagur A.R., Wang H., Cao Y., Lin S., Li X. Success rate and safety of coronary angiography and angioplasty via radial artery approachamong a Chinese population. J Invasive Cardiol 2014; 26 (6): 273–5. PMID: 24907084Nikolakopoulos I., Vemmou E., Karacsonyi J. et al. Latest developments in chronic total occlusion percutaneous coronary intervention. Expert Rev Cardiovasc Ther 2020; 15 (7): 415–26. DOI: 10.1080/14779072.2020.1787153 PMID: 32594784Lee S.H., Cho J.Y., Kim J.S. et al. A comparison of procedural success rate and long-term clinical outcomes between in-stent restenosis chronic total occlusion and de novo chronic total occlusion using multicenter registry data. Clin Res Cardiol 2020; 109 (5): 628–37. DOI: 10.1007/s00392-019-01550-7 PMID: 31552494Kosyakovsky L.B., Austin P.C., Ross H.J. et al. Early invasive coronary angiography and acute ischaemic heart failure outcomes. Eur Heart J 2021; 42 (36): 3756–66. DOI: 10.1093/eurheartj/ehab423 PMID: 34331056Nerlekar N., Ha F.J., Verma K.P. et al. Percutaneous coronary intervention using drug-eluting stents versus coronary artery bypass grafting for unprotected left main coronary artery stenosis: A metaanalysis of randomized trials. Circ Cardiovasc Interv. 2016; 9 (12): e004729. DOI: 10.1161/CIRCINTERVENTIONS.116.004729 PMID: 27899408Gao L., Liu Y., Sun Z., Wang Y., Cao F., Chen Y. Percutaneous coronary intervention using drug-eluting stents versus coronary artery bypass graft surgery in left main coronary artery disease an updated meta-analysis of randomized clinical trials. Oncotarget 2017; 8 (39): 66449–57. DOI: 10.18632/oncotarget.20142 PMID: 29029526Thuijs D.J.F.M., Kappetein A.P., Serruys P.W. et al. Percutaneous coronary intervention versus coronary artery bypass grafting in patients with three-vessel or left main coronary artery disease: 10-year follow-up of the multicentre randomised controlled SYNTAX trial. Lancet 2019; 394 (10206): 1325–34. DOI: 10.1016/S0140-6736(19)31997-X PMID: 31488373Spadaccio C., Benedetto U. Coronary Artery Bypass Grafting (CABG) vs. Percutaneous Coronary Intervention (PCI) in the treatment of multivessel coronary disease: quo vadis–a review of the evidences on coronary artery disease. Ann Cardiothorac Surg. 2018; 7 (4): 506–15. DOI: 10.21037/acs.2018.05.17 PMID: 30094215Baykan A.O., Gür M., Acele A. et al. Predictors of successful percutaneous coronary intervention in chronic total coronary occlusions. Postepy Kardiol Interwencyjnej 2016; 1 (1): 17–24. DOI: 10.5114/pwki.2016.56945 PMID: 26966445Cimen S., Gooya A., Grass M., Frangi A. Reconstruction of coronary arteries from X-ray angiography: A review. Medical Image Analysis 2016; 32. DOI: 10.1016/j.media.2016.02.007Vukicevic A.M., Çimen S., Jagic N. et al. Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images. Sci Rep 2018; 8: 1711. DOI: 10.1038/s41598-018-19440-9Kass M., Witkin A., Terzopoulos D. Snakes: Active contour models. Int. J. Comput. Vis. 1988; 1 (4): 321–331. DOI: 10.1007/bf00133570Xu C., Prince J.L. Generalized gradient vector flow external forces for active contours. Signal Process. 1998; 71: 131–9. DOI: 10.1016/S0165-1684(98)00140-6Bappy D.M., Hong A., Choi E., Park J.-O., Kim C.-S. Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections. Comput. Med. Imaging Graph. 2021; 92: 101956. DOI: 10.1016/j.compmedimag.2021.101956Iyer K., Nallamothu B.K., Figueroa C.A. et al. A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images. Sci Rep 2023; 13: 17603. DOI: 10.1038/s41598-023-44633-2Wang Y., Banerjee A., Choudhury R., Grau V. (). Deep Learning-based 3D Coronary Tree Reconstruction from Two 2D Non-simultaneous X-ray. Angiography Projections 2024; 07. DOI: 10.48550/arXiv.2407.1461629.Gruntzig A.R., Senning A., Siegenthaler W.E. Nonoperative dilatation of coronary- artery stenosis: percutaneous transluminal coronary angioplasty. N Engl J Med. 1979; 301: 61–68. DOI: 10.1056/nejm197907123010201Fearon W.F., Nishi T., De Bruyne B. et al. Clinical outcomes and cost-effectiveness of fractional flow Reserve-guided percutaneous coronary intervention in patients with stable coronary artery disease: three-year follow-up of the FAME 2 trial (fractional flow Reserve versus angiography for multivessel evaluation). Circulation 2018; 137: 480–487. DOI: 10.1161/circulationaha.117.031907Tonino P.A., De Bruyne B., Pijls N.H. et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. 2009; 360: 213–224. DOI: 10.1056/NEJMoa0807611Knuuti J., Wijns W., Saraste A. et al. ESC guidelines for the diagnosis and management of chronic coronary syndromes: the task force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2019; 2019. DOI: 10.1093/eurheartj/ehz425Gulati M., Levy P.D., Mukherjee D. et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/ SCMR guideline for the evaluation and diagnosis of CHEST pain: executive summary: a report of the American College of Cardiology / American Heart Association joint committee on clinical practice guidelines. J Am Coll Cardiol. 2021; 78: 2218–2261. DOI: 10.1016/j.jacc.2021.07.052Parikh R.V., Liu G., Plomondon M.E. et al. Utilization and outcomes of measuring fractional flow Reserve in Patients with Stable Ischemic Heart Disease. J Am Coll Cardiol. 2020; 75: 409–419. DOI: 10.1016/j.jacc.2019.10.060Toth G.G., Toth B., Johnson N.P. et al. Revascularization decisions in patients with stable angina and intermediate lesions: results of the international survey on interventional strategy. Circ Cardiovasc Interv. 2014; 7: 751–759. DOI: 10.1161/circinterventions.114.001608Meier B., Gruentzig A.R., Goebel N., Pyle R., von Gosslar W., Schlumpf M. Assessment of stenoses in coronary angioplasty. Inter- and intraobserver variability. Int J Cardiol. 1983; 3: 159–169. DOI: 10.1016/0167-5273(83)90032-3Rutishauser W., Noseda G., Bussmann W.D., Preter B. Blood flow measurement through single coronary arteries by roentgen densitometry. Right coronary artery flow in conscious man. Am J Roentgenol Radium Ther Nucl Med. 1970; 109: 21–24. DOI: 10.2214/ajr.109.1.21Tu S., Barbato E., K¨oszegi Z. et al. Fractional flow reserve calculation from 3- dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv. 2014; 7: 768–777. DOI: 10.1016/j.jcin.2014.03.004 PMID: 25060020Schuurbiers J.C., Lopez N.G., Ligthart J., et al. In vivo validation of CAAS QCA-3D coronary reconstruction using fusion of angiography and intravascular ultrasound (ANGUS). Catheter Cardiovasc Interv. 2009; 73: 620–626. DOI: 10.1016/j.jcin.2014.03.004Migliavacca F., Petrini L., Massarotti P., Schievano S., Auricchio F., Dubini G. Stainless and shape memory alloy coronary stents: a computational study on the interaction with the vascular wall. Biomech. Model. Mechanobiol. 2004; 2: 205–217. DOI: 10.1007/s10237-004-0039-6 PMID: 15029511Wu W., Wang W., Yang D., Qi M. Stent expansion in curved vessel and their interactions: a finite element analysis J. Biomech. 2007; 40: 2580–2585. DOI: 10.1016/j.jbiomech.2006.11.009 PMID: 17198706Djukic T., Saveljic I., Pelosi G., Parodi O., Filipovic N. Numerical simulation of stent deployment within patient-specific artery and its validation against clinical data. Comput. Methods Progr. Biomed. 2019; 175: 121–127. DOI: 10.1016/j.cmpb.2019.04.005 PMID: 31104701Djukic T., Saveljic I., Pelosi G., Parodi O., Filipovic N. A study on the accuracy and efficiency of the improved numerical model for stent implantation using clinical data. Comput. Methods Progr. Biomed. 2021; 207: Article 106196. DOI: 10.1016/j.cmpb.2021.106196 PMID: 34091419