Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework
DOI:
https://doi.org/10.48165/gjs.2024.1207Keywords:
time monitoring, augmentation, medical assessments, patient data and historyAbstract
The convergence of digital twin technology and machine learning has ushered in a transformative era in patient monitoring and diagnosis within the healthcare sector. This review article explores the comprehensive integration of digital twin-driven machine learning frameworks, aiming to elucidate the core objectives, pivotal findings, and overarching implications. Our primary objectives encompass the exploration of digital twin technology's adaptation to healthcare, the augmentation of medical assessments through machine learning algorithms, the enabling of real time monitoring with early anomaly detection capabilities, and the personalization of treatment plans rooted in patient profiles generated by digital twins. The key findings underscore the successful adaptation of digital twin technology for healthcare applications, emphasizing its potential to capture dynamic patient data and history. The synergy between machine learning and digital twins enhances the precision of diagnostics and predictive analytics, thus improving healthcare outcomes. Real-time monitoring, made possible through digital twins, ensures proactive patient care with timely interventions. Moreover, personalizing treatment plans, tailored to individual patient profiles, offers a promising avenue for more effective and less invasive interventions. The implications of this review extend to the transformative potential of digital twin driven machine learning in healthcare, with the ability to revolutionize patient care, diagnostics, and monitoring. The review highlights data security and ethical challenges, stressing the need for standardized protocols to protect patient information. Ongoing research and innovation are crucial for maximizing these frameworks' potential, improving patient outcomes, and enhancing healthcare quality.References
[1] Cellina, M., Cè, M., Alì, M., Irmici, G., Ibba, S., Caloro, E., Fazzini, D., et al. (2023). Digital Twins: The New Frontier for Personalized Medicine? Applied Sciences, 13(13), 7940. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app13137940
[2] Kamel Boulos, M. N., & Zhang, P. (2021). Digital Twins: From Personalised Medicine to Precision Public Health. Journal of personalized medicine, 11(8), 745. https://doi.org/10.3390/jpm11080745
[3] Iqbal, J., Cortés Jaimes, D. C., Makineni, P., Subramani, S., Hemaida, S., Thugu, T. R., Butt, A. N., Sikto, J. T., Kaur, P., Lak, M. A., Augustine, M., Shahzad, R., & Arain, M. (2023). Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus, 15(9), e44658. https://doi.org/10.7759/cureus.44658
[4] Abid, H., Mohd, J., Ravi, P., Singh, R. S. (2023), Exploring the revolution in healthcare systems through the applications of digital twin technology, Biomedical Technology, 4(2023), 28-38
[5] Vallée A. (2023). Digital twin for healthcare systems. Frontiers in digital health, 5, 1253050. https://doi.org/10.3389/fdgth.2023.1253050 [6] Al-Muammar, A.M., Ahmed, Z. and Aldahmash, A. M. (2018). Paradigm Shift in Healthcare through Technology and Patient-Centeredness. Int Arch Public Health Community Med 2:015 doi.org/10.23937/iaphcm-2017/1710015
[7] Wei, S. (2021). Is Human Digital Twin possible? Computer Methods and Programs in Biomedicine Update. 1(2021). ISSN 2666-9900 [8] Popa, E. O., van -Hilten, M., Oosterkamp, E., & Bogaardt, M. J. (2021). The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks. Life sciences, society and policy, 17(1), 6. https://doi.org/10.1186/s40504-021-00113-x
[9] Jingshan, L. and Pascale, C. (2021). Health Care 4.0: A vision for smart and connected health care. IISE Transactions on Healthcare Systems Engineering. 11(3). Pages 171-180. https://doi.org/10.1080/24725579.2021.1884627
[10] Volkov, I., Radchenko, G. and Tchernykh, A. (2021). Digital Twins, Internet of Things and Mobile Medicine: A Review of Current Platforms to Support Smart Healthcare. Program Comput Soft 47(2021), 578–590. https://doi.org/10.1134/S0361768821080284
70 Goni et al, Global Journal of Sciences, 1(2), 2024, 58-70
[11] Wang, E., Tayebi, P. and Song, Y. T. (2023). Cloud-Based Digital Twins’ Storage in Emergency Healthcare. Int J Netw Distrib Comput. https://doi.org/10.1007/s44227-023-00011-y
[12] Sheng, B., Wang, Z., Qiao, Y., Xie, S. Q., Tao, J., & Duan, C. (2023). Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review. Digital health. 9(2023) 20552076231203672. https://doi.org/10.1177/20552076231203672. [13] Giacinto, B., Andrea, G., Federico, S., Alice, R., and Claudio, and Pacchierotti, L. D. (2023). Digital Twins and Healthcare: Quick Overview and Human-Centric Perspectives. mHealth and Human Centered Design Towards Enhanced Health, Care, and Well-being, 120, Springer Nature, pp.57- 78, 2023.
[14] Turab, M., and Jamil, S. (2023). A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse. BioMedInformatics, 3(3), 563– 584. MDPI AG. Retrieved from http://dx.doi.org/10.3390/biomedinformatics3030039
[15] Samariya, D., Ma, J., Aryal, S., and Zhao, X. (2023). Detection and explanation of anomalies in healthcare data. Health information science and systems. 11(1), 20. https://doi.org/10.1007/s13755-023-00221-2
[16] Firdous, S., Wagai, G. A., and Sharma, K. (2022). A survey on diabetes risk prediction using machine learning approaches. Journal of family medicine and primary care. 11(11), 6929–6934. https://doi.org/10.4103/jfmpc.jfmpc_502_22
[17] Mazhar, M. R., Syed, A. S., Dhirendra, S., Elmahdi, B. and Spiridon, B. (2021). Role of AI, Machine Learning, and Big Data in Digital Twinning: A SLR, Challenges, and Opportunities. IEEE Access. 9(2021), 32030-32052. doi: 10.1109/ACCESS.2021.3060863.
[18] Habehh, H., and Gohel, S. (2021). Machine Learning in Healthcare. Current genomics, 22(4), 291–300. https://doi.org/10.2174/1389202922666210705124359
[19] Alzubaidi, L., Zhang, J., and Humaidi, A.J. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 8(53) https://doi.org/10.1186/s40537-021-00444-8
[20] Aldahiri, A., Alrashed, B., and Hussain, W. (2021). Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting, 3(1), 181–206. MDPI AG. Retrieved from http://dx.doi.org/10.3390/forecast3010012
[21] Taloba, A. I., Elhadad, A., Rayan, A., Abd El-Aziz, R. M., Salem, M., Alzahrani, A. A., ... & Park, C. (2023). A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare. Alexandria Engineering Journal, 65, 263-274.
[22] Bamanga, M. A. Ahmad, A. S., Malgwi, Y. M., and Babando, K. A. (2021). Predictive analysis of heart disease using selected Machine Learning Meta-Algorithms. Journal of Tianjin University Science and Technology. Vol. 5(7). 10.17605/OSF.IO/U6537
[23] Sheuly, S. S., Ahmed, M. U and Begum, S. (2022). Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview. Applied Sciences, 12(13), 6512. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app12136512 [24] Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., and Lozoya-Santos, J. de J. (2022). Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sensing. 14(6), 1335. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs14061335
[25] Armeni, P., Polat, I., De Rossi, L. M., Diaferia, L., Meregalli, S., and Gatti, A. (2022). Digital Twins in Healthcare: Is It the Beginning of a New Era of Evidence-Based Medicine? A Critical Review. Journal of Personalized Medicine. 12(8), 1255. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jpm12081255
[26] Liu, L., Guo, K., Gao, Z., Li, J., and Sun, J. (2022). Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops. Sustainability. 14(9), 5340. MDPI AG. Retrieved from http://dx.doi.org/10.3390/su14095340
[27] Goetz, L. H., and Schork, N. J. (2018). Personalized medicine: motivation, challenges, and progress. Fertility and sterility. 109(6), 952–963. https://doi.org/10.1016/j.fertnstert.2018.05.006
[28] Grady, P. A., and Gough, L. L. (2014). Self-management: a comprehensive approach to management of chronic conditions. American journal of public health. 104(8), e25–e31. https://doi.org/10.2105/AJPH.2014.302041
[29] Bamanga, M.A., Ahmadu, A.S., and Yusuf, Y.M. (2021). Ensemble Model for Heart Disease Prediction. LC International Journal of STEM . Vol. 2(4), 13–23.
[30] Cascini, F., Santaroni, F., Lanzetti, R., Failla, G., Gentili, A., and Ricciardi, W. (2021). Developing a Data-Driven Approach in Order to Improve the Safety and Quality of Patient Care. Frontiers in public health. 9, 667819. https://doi.org/10.3389/fpubh.2021.667819 [31] Bali, B., & Garba, E. J. (2021). Neuro-fuzzy approach for prediction of neurological disorders: a systematic review. SN Computer Science, 2(4), 307.
[32] Huang, P. H., Kim, K. H., and Schermer, M. (2022). Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study. Journal of medical Internet research. 24(1), e33081. https://doi.org/10.2196/33081
[33] Greg, P. (2021). Meet data-centric engineering: Engineering better relationships and more sustainable capital projects. White Paper. https://www.aveva.com/en/products/unified-engineering/
[34] Smye, S. W., and Frangi, A. F. (2021). Interdisciplinary research: shaping the healthcare of the future. Future healthcare journal. 8(2), e218–e223. https://doi.org/10.7861/fhj.2021-0025
[35] Al-Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., and Al-Muhanna, F. A. (2023). A Review of the Role of Artificial Intelligence in Healthcare. Journal of personalized medicine. 13(6), 951. https://doi.org/10.3390/jpm13060951 [35] Patel, H., Singh Rajput, D., Thippa Reddy, G., Iwendi, C., Kashif Bashir, A., & Jo, O. (2020). A review on classification of imbalanced data for wireless sensor networks. International Journal of Distributed Sensor Networks, 16(4), 1550147720916404.
[36] Al-Turjman, F., & Alturjman, S. (2018). Confidential smart-sensing framework in the IoT era. The Journal of Supercomputing, 74(10), 5187-5198. [37] Ozsahin, D. U., Duwa, B. B., Idoko, J. B., Hamdan, M. K., Aljammal, G., Elsafdy, K., & Ozsahin, I. (2024). IoT-based infant monitoring device. In Practical Design and Applications of Medical Devices (pp. 39-58). Academic Press.
[38] Duwa, B. B., Ozsoz, M., & Al-Turjman, F. (2020). Applications of AI, IoT, IoMT, and Biosensing Devices in Curbing COVID-19. In AI-Powered IoT for COVID-19 (pp. 141-158). CRC Press.
