This research created and tested a digital twin model system for patients with acute ischemic stroke (AIS). The study was conducted in three comprehensive stroke units and included 200 patients with AIS, followed for one year. The model incorporates AI algorithms to optimize treatment strategies via continuous pain monitoring and real-time integration of multimodal neuroimaging and clinical data. Findings included improvements when compared to standard care for patient outcomes using a digital twin guided approach: The proportion of patients who made good functional recovery (mRS≤2) on day 90 after the stroke increased by 16.2 percentage points (with 95% credibility interval, CI: 12.7-19.7% p<0.001) whereas in-hospital mortality risk was reduced by a 35% (relative risk ratio 0.65, 95% CI: 0.52-0.81, p<0.001). The ability of the model to predict the progression of the disease was also good attaining ROC AUC of 0.89 (95% CI: 0.85-0.93). The system had great integration capabilities as the average latency time was only 87ms and 98.5% data capture completeness. This work confirms the important value of digital twin technology in managing acute stroke patient and it also opens new avenues for achieving personalized medicine. This technology offers improvements in healthcare delivery by enabling data-driven continuous decision support for patient care during hospital admission.

Wei Guan. Construction of Digital Twin Model for Acute Ischemic Stroke Patients and Its Application in Disease Management. Frontiers in Medical Science Research(2025), Vol. 7, Issue 2: 61-72. https://doi.org/10.25236/FMSR.2025.070210.

[1] Sun, T., He, X., & Li, Z. (2023). Digital twin in healthcare: Recent updates and challenges. Digital Health, 9, 1-13. https://doi.org/10.1177/20552076221149651

[2] Sun, T., He, X., Song, X., Shu, L., Li, Z., & Liu, R. (2022). The Digital Twin in Medicine: A Key to the Future of Healthcare? Frontiers in Medicine, 9, 907066.

[3] Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., ... & Kalthoff, K. (2020). Digital twins to personalize medicine. Genome Medicine, 12(1), 1-4.

[4] Cellina, M., Cè, M., Alì, M., Irmici, G., Ibba, S., Caloro, E., Fazzini, D., Oliva, G., & Papa, S. (2023). Digital Twins: The New Frontier for Personalized Medicine? Applied Sciences, 13(13), 7940. https://doi.org/10.3390/app13137940

[5] Allen, A., Siefkas, A., Pellegrini, E., Burdick, H., Barnes, G., Calvert, J., Mao, Q., & Das, R. (2021). A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Applied Sciences, 11(12), 5576.

[6] Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., ... & Lamata, P. (2020). The 'digital twin' to enable the vision of precision cardiology. European Heart Journal, 41(48), 4556-4564.

[7] Hernandez-Boussard, T., Macklin, P., Greenspan, E. J., Gryshuk, A. L., Stahlberg, E., Syeda-Mahmood, T., ... & Shmulevich, I. (2021). Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nature Medicine, 27(12), 2065-2066.

[8] Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., & Ziemssen, T. (2021). Digital Twins for Multiple Sclerosis. Frontiers in Immunology, 12, 669811.

[9] Mazumder, O., Roy, D., Bhattacharya, S., Sinha, A., & Pal, A. (2019). Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: A digital twin of cardiovascular system. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5024-5029).

[10] Peirlinck, M., Costabal, F.S., Yao, J., Guccione, J.M., Tripathy, S., Wang, Y., Ozturk, D., Segars, P., Sundnes, J., Kuhl, E., & Trayanova, N.A. (2021). Precision medicine in human heart modeling. Biomechanics and Modeling in Mechanobiology, 20, 803-831.

[11] Fisher CK, Smith AM, Walsh JR. Machine learning for comprehensive forecasting of Alzheimer's Disease progression. Sci Rep. 2019;9:13622.

[12] Zhang Y, Zhou Y, Zhang D, Song W. A Stroke Risk Detection: Improving Hybrid Feature Selection Method. J Med Internet Res. 2019;21:e12437.

[13] Croatti A, Gabellini M, Montagna S, et al. On the Integration of Agents and Digital Twins in Healthcare. J Med Syst. 2020;44:161.

[14] Cruz, M., Rivas, D., Cárdenas, Y., & González, J. (2021). Digital twins in healthcare: A systematic literature review. Applied Sciences, 11(19), 9222.

[15] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access, 8, 108952-108971.

[16] Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 1-13.

[17] Bruynseels, K., Santoni de Sio, F., & van den Hoven, J. (2018). Digital twins in health care: ethical implications of an emerging engineering paradigm. Frontiers in Genetics, 9, 31.

[18] Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., ... & Deen, M. J. (2019). A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access, 7, 49088-49101.

[19] Cosío-León, M., Ojeda-Carreño, D., Nieto-Hipólito, J. I., & Ibarra-Hernández, J. A. (2018). The use of standards in the design of the Internet of Things. International Journal on Advanced Science, Engineering and Information Technology, 8(1), 1-7.

[20] Rivera, L. F., Jiménez, M., Angara, P., Villegas, N. M., Tamura, G., & Müller, H. A. (2020). Towards continuous monitoring in personalized healthcare through digital twins. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 1-12).

[21] Zhang, A., Li, S., Cui, Y., Yang, W., Dong, R., Hu, J., … & Zhang, H. (2022). Digital twin technology in medicine: Applications, challenges, and future prospects. Frontiers in Medicine, 9, 1004828.

[22] Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52.

[23] El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE multimedia, 25(2), 87-92.

[24] Fagherazzi, G. (2020). Deep digital phenotyping and digital twins for precision health: Time to dig deeper. Journal of Medical Internet Research, 22(3), e16770.