Система поддержки принятия врачебных решений в кардиологии на основе цифрового двойника сердечно-сосудистой системы
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

The system for supporting medical decision-making in cardiology based on a digital twin of the cardiovascular system

idFrolov S.V. idKorobov A.A. Vetrov A.N.  

UDC 615.471
DOI: 10.26102/2310-6018/2023.40.1.007

  • Abstract
  • List of references
  • About authors

The analysis of existing digital counterparts of the cardiovascular system and medical decision support systems in cardiology is carried out. There is a low degree of elaboration or lack of consideration for the mechanisms for regulating blood circulation in them. The structure of a new biotechnical system is proposed, which makes it possible to form recommendations for the doctor as to decide on therapeutic effects in order to optimize the functions (indices) of the patient's cardiovascular system. The problem of optimizing the patient's condition for the medical decision support system is defined and the solution to it is provided. The structure of a biotechnical system for optimizing the patient's condition using a digital twin of the cardiovascular system as a virtual personalized model of the circulatory system connected to the patient by two-way information communication is described. A diagram of the elements of the biotechnical system detailing the ways of transmitting diagnostic information from the patient to the digital twin of the cardiovascular system is presented. The hardware for checking the adequacy (validation), verification and identification of the digital twin of the cardiovascular system is given. An example of the search for optimal properties necessary to optimize the indices of the functions of the cardiovascular system of an average patient is considered. The current and found optimal values of the indices of the patient's condition are obtained. To achieve indices that ensure the normalization of the patient's condition, optimal values of the properties of the cardiovascular system were found.

1. Lishchuk V.A., Gazizova D.Sh. (Ed.). The technology of individual therapy. Moscow, OOO PRINT PRO; 2016. 249 p. (In Russ.).

2. Kramm M.N., Bezborodova O.E., Bodin O.N., Svetlov A.V. Digital heart double. Izmereniya. Monitoring. Upravlenie. Kontrol' = Measuring. Monitoring. Management. Control. 2021;1:73–84. Available from: https://elib.pnzgu.ru/files/eb/GIZ6IzEbZX7T.pdf. DOI: 10.21685/2307-5538-2021-1-9 (accessed on 13.01.2023). (In Russ.).

3. Frolov S.V., Aliev N.E., Korobov A.A., Sindeev S.V. Approached to lumped parameter modeling of cardiovascular system and their application for evaluation of the cerebral circulation. Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki = News of the Tula state university. Technical sciences. 2018;10:240–248. Available from: https://cyberleninka.ru/article/n/podhody-k-nulmernomu-modelirovaniyu-serdechno-sosudistoy-sistemy-i-ih-ispolzovanie-pri-otsenke-mozgovogo-krovoobrascheniya/pdf (accessed on 13.01.2023). (In Russ.).

4. Frolov S.V., Korobov A.A., Gazizova D.Sh., Potlov A.Yu. Cardiovascular model with regulation using a neural network. Modeli, sistemy, seti v ekonomike, tekhnike, prirode i obshchestve = Models, systems, networks in economics, technology, nature and society. 2021;2:79–94. Available from: https://mss.pnzgu.ru/mss5221. DOI: 10.21685/2227-8486-2021-2-5 (accessed on: 13.01.2023). (In Russ.).

5. Baillargeon B., Rebelo N., Fox D.D., Taylor R.L., Kuhl E. The Living Heart Project: A robust and integrative simulator for human heart function. European Journal of Mechanics. 2014;48:38–47. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4175454/pdf/nihms589535.pdf. DOI: 10.1016/j.euromechsol.2014.04.001 (accessed on 13.01.2023).

6. Narang A., Mor-Avi V., Prado A., Volpato V., Prater D., Tamborini G., Fusini L., Pepi M., Goyal N., Addetia K., Gonçalves A., Patel A.R., Lang R.M. Machine learning based automated dynamic quantification of left heart chamber volumes. European Heart Journal – Cardiovascular Imaging. 2019;20(5):541–549. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933871/pdf/jey137.pdf. DOI: 10.1093/ehjci/jey137 (accessed on 13.01.2023).

7. Renaudin C.P., Barbier B., Roriz R., Revel D., Amiel M. Coronary arteries: new design for three-dimensional arterial phantoms. Radiology. 1994;190(2):579–582. DOI: 10.1148/radiology.190.2.8284422.

8. Chakshu N.K., Sazonov I., Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomechanics and Modeling in Mechanobiology. 2021;20:449–465. Available from: https://link.springer.com/content/pdf/10.1007/s10237-020-01393-6.pdf?pdf=button. DOI: 10.1007/s10237-020-01393-6 (accessed on 13.01.2023).

9. Mazumder O., Roy D., Bhattacharya S., Sinha A., Pal A. Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society Virtual. 2019:5024–5029. DOI: 10.1109/EMBC.2019.8856691.

10. Garber L., Khodaei S., Keshavarz-Motamed Z. The critical role of lumped parameter models in patient-specific cardiovascular simulation. Archives of Computational Methods in Engineering. 2022:2977–3000. DOI: 10.1007/s11831-021-09685-5.

11. Yasnitsky L.N., Cherepanov F.M. Neuro-expert system of diagnostics, forecasting and risk management cardiovascular diseases. Prikladnaya matematika i voprosy upravleniya = Applied Mathematics and Control Sciences. 2018;3:107–126. Available from: https://vestnik.pstu.ru/get/_res/fs/file.pdf/7796/%CD%E5%E9%F0%EE%FD%EA%F1%EF%E5%F0%F2%ED%E0%FF+%F1%E8%F1%F2%E5%EC%E0+%E4%E8%E0%E3%ED%EE%F1%F2%E8%EA%E8%2C+%EF%F0%EE%E3%ED%EE%E7%E8%F0%EE%E2%E0%ED%E8%FF+%E8+%F3%EF%F0%E0%E2%EB%E5%ED%E8%FF+%F0%E8%F1%EA%E0%EC%E8+%F1%E5%F0%E4%E5%F7%ED%EE-%F1%EE%F1%F3%E4%E8%F1%F2%FB%F5+%E7%E0%E1%EE%EB%E5%E2%E0%ED%E8%E9file.pdf. DOI: 10.15593/2499-9873/2018.3.08 (accessed on 13.01.2023). (In Russ.).

12. Lishchuk V.A. Mathematical theory of blood circulation. Moscow, Meditsyna; 1991. 265 p. (In Russ.).

13. Burakovskii V.I., Lishchuk V.A., Gazizova D.Sh. Aybolit – a new technology for classification, diagnostics and active individual treatment. Moscow, The Institute of Cardiovascular Surgery; 1991. 64 p. (In Russ.).

14. Kiselev K.V., Noeva E.A., Vyborov O.N., Zorin A.V., Potekhina A.V., Osyaeva M.K., Shvyrev S.L., Martynyuk T.V., Chazova I.E., Zarubina T.V. Development of knowledge base architecture for clinical decision support system based on graph database. Meditsinskie tekhnologii. Otsenka i vybor = Medical Technologies. Assessment and Choice. 2018;3(33):42–48. Available from: https://cyberleninka.ru/article/n/razrabotka-arhitektury-bazy-znaniy-sistemy-podderzhki-prinyatiya-vrachebnyh-resheniy-osnovannoy-na-grafovoy-baze-dannyh/pdf. DOI: 10.31556/2219-0678.2018.33.3.042-048 (accessed on 13.01.2023). (In Russ.).

15. Kirikov I.A., Kolesnikov A.V., Rumovskaya S.B. Functional hybrid intelligent decision support system for diagnostics of arterial hypertension. Sistemy i Sredstva Informatiki = Systems and Means of Informatics. 2014;24(1):153–179. Available from: https://www.mathnet.ru/links/fe3a80a5527c85600ea3b24dece2c7a8/ssi335.pdf. DOI: 10.14357/08696527140110 (accessed on 13.01.2023). (In Russ.).

16. Losik D.V., Kozlova S.N., Krivosheev Yu.S., Ponomarenko A.V., Ponomarev D.N., Pokushalov E.A., Bolshakova O.O., Zhabina E.S., Lyasnikova E.A., Korelskaya N.A., Trukshina M.A., Tulintseva T.E., Konradi A.O. Retrospective analysis of clinical decision support system use in patients with hypertension and atrial fibrillation (INTELLECT). Rossiiskii kardiologicheskii zhurnal = Russian Journal of Cardiology. 2021;26(4):54–60. Available from: https://russjcardiol.elpub.ru/jour/article/view/4406/3292. DOI: 10.15829/1560-4071-2021-4406 (accessed on 13.01.2023). (In Russ.).

17. Gavrilov D.V., Serova L.M., Korsakov I.N., Gusev A.V., Novitskii R.E., Kuznetsova T.Yu. Cardiovascular diseases prediction by integrated risk factors assessment by means of machine learning. Vrach = The Doctor. 2020;5:41–46. DOI: 10.29296/25877305-2020-05-08. (In Russ.).

18. McLachlan A., Wells S., Furness S., Jackson R., Kerr A. Equity of access to CVD risk management using electronic clinical decision support in the coronary care unit. European Journal of Cardiovascular Nursing. 2010;9(4):233–237. Available from: https://academic.oup.com/eurjcn/article/9/4/233/5929284?login=false. DOI: 10.1016/j.ejcnurse.2010.01.007 (accessed on 13.01.2023).

19. Korobov A.A., Frolov S.V., Aliyev N.E., Rodionova I.E. Dual-contoured model of cardiovascular system regulation. Journal of Physics: Conference Series. 2020;1553:012006. Available from: https://iopscience.iop.org/article/10.1088/1742-6596/1553/1/012006/pdf. DOI: 10.1088/1742-6596/1553/1/012006 (accessed on: 13.01.2023).

20. Frolov S.V., Sindeev S.V., Korobov A.A., Potlov A.Yu. Combined method of neurocontrol for nonlinear non-stationary object. 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). 2020:582–585. Available from: https://ieeexplore.ieee.org/document/9280705. DOI: 10.1109/SUMMA50634.2020.9280705 (accessed on 13.01.2023).

21. Frolov S.V., Potlov A.Yu., Korobov A.A., Savinova K.S. Gradient-based method for neural network self-learning control of multi-loop nonlinear time-varying stochastically disturbed system. Pribory i sistemy. Upravlenie, kontrol', diagnostika = Instruments and Systems: Monitoring, Control, and Diagnostics. 2021;5:41–48. DOI: 10.25791/pribor.5.2021.1262. (In Russ.).

22. Frolov S.V., Sindeev S.V., Korobov A.A., Savinova K.S., Potlov A.Y. A two-stage procedure for the synthesis of control of nonlinear non-stationary objects using a multilayer perceptron. Modeling, Optimization and Information Technology. 2020;8(3). Available from https://moit.vivt.ru/wp-content/uploads/2020/08/FrolovSoavtors_3_20_1.pdf DOI: 10.26102/2310- 6018/2020.30.3.028 (In Russ). (accessed on 13.01.2023).

23. Wiener N. Cybernetics: or the Control and Communication in the Animal and the Machine. 2nd revised ed. Paris, The MIT Press; 1961. 212 p.

24. Akhutin V.M. (Ed.) Biotechnical systems. Theory and design. A manual. Leningrad, LGU; 1981. Available from: http://elib.osu.ru/bitstream/123456789/6087/1/638_20110709.pdf. (accessed on 13.01.2023). (In Russ.).

Frolov Sergei Vladimirovich
Doctor of Technical Sciences, Professor
Email: Sergej.frolov@gmail.com

WoS | Scopus | ORCID | eLibrary |

Tambov State Technical University

Tambov, Russian Federation

Korobov Artyom Andreevich

ORCID |

Tambov State Technical University

Tambov, Russian Federation

Vetrov Aleksandr Nikolaevich
Candidate of Technical Sciences

Tambov State Technical University

Tambov, Russian Federation

Keywords: decision support system, regulation, mathematical modeling, cardiovascular system, neurocontrol, optimization problem

For citation: Frolov S.V. Korobov A.A. Vetrov A.N. The system for supporting medical decision-making in cardiology based on a digital twin of the cardiovascular system. Modeling, Optimization and Information Technology. 2023;11(1). Available from: https://moitvivt.ru/ru/journal/pdf?id=1308 DOI: 10.26102/2310-6018/2023.40.1.007 (In Russ).

475

Full text in PDF

Received 16.01.2023

Revised 01.02.2023

Accepted 08.02.2023

Published 08.02.2023