REFERENCES

1. Yeates K, Lohfeld L, Sleeth J, Morales F, Rajkotia Y, Ogedegbe O. A Global perspective on cardiovascular disease in vulnerable populations. Can J Cardiol. 2015;31:1081-93.

2. Spînu M, Onea LH, Homorodean C, Olinic M, Ober MC, Olinic DM. Optical coherence tomography-OCT for characterization of non-atherosclerotic coronary lesions in acute coronary syndromes. J Clin Med. 2022;11:265.

3. Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res. 2023;28:242.

4. Szczykutowicz TP. Computed tomography angiography: principles and advances. Radiol Clin North Am. 2024;62:371-83.

5. Kocaoglu M, Pednekar A, Fleck RJ, Dillman JR. Cardiothoracic magnetic resonance angiography. Curr Probl Diagn Radiol. 2024;53:154-65.

6. Upadhyay RK. Emerging risk biomarkers in cardiovascular diseases and disorders. J Lipids. 2015;2015:971453.

7. Barbiero P, Viñas Torné R, Lió P. Graph representation forecasting of patient’s medical conditions: toward a digital twin. Front Genet. 2021;12:652907.

8. Yao JF, Yang Y, Wang XC, Zhang XP. Systematic review of digital twin technology and applications. Vis Comput Ind Biomed Art. 2023;6:10.

9. Emmert-Streib F, Yli-Harja O. What is a digital twin? Experimental design for a data-centric machine learning perspective in health. Int J Mol Sci. 2022;23:13149.

10. Moztarzadeh O, Jamshidi MB, Sargolzaei S, et al. Metaverse and healthcare: machine learning-enabled digital twins of cancer. Bioengineering. 2023;10:455.

11. Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access. 2019;7:167653-71.

12. Liu M, Fang S, Dong H, Xu C. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst. 2021;58:346-61.

13. Emmert-streib F. Defining a digital twin: a data science-based unification. Mach Learn Knowl Extr. 2023;5:1036-54.

14. Sel K, Osman D, Zare F, et al. Building digital twins for cardiovascular health: from principles to clinical impact. J Am Heart Assoc. 2024;13:e031981.

15. Niederer SA, Sacks MS, Girolami M, Willcox K. Scaling digital twins from the artisanal to the industrial. Nat Comput Sci. 2021;1:313-20.

16. Bruynseels K, Santoni de Sio F, van den Hoven J. Digital twins in health care: ethical implications of an emerging engineering paradigm. Front Genet. 2018;9:31.

17. Corral-Acero J, Margara F, Marciniak M, et al. The ‘Digital Twin’ to enable the vision of precision cardiology. Eur Heart J. 2020;41:4556-64.

18. Hormuth DA 2nd, Jarrett AM, Lorenzo G, et al. Math, magnets, and medicine: enabling personalized oncology. Expert Rev Precis Med Drug Dev. 2021;6:79-81.

19. Shamanna P, Saboo B, Damodharan S, et al. Reducing HbA1c in Type 2 diabetes using digital twin technology-enabled precision nutrition: a retrospective analysis. Diabetes Ther. 2020;11:2703-14.

20. Oikonomou E, Theofilis P, Lampsas S, et al. Current concepts and future applications of non-invasive functional and anatomical evaluation of coronary artery disease. Life. 2022;12:1803.

21. Libby P, Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473:317-25.

22. Dong Y, Ma G, Hou X, et al. Kindlin-2 controls angiogenesis through modulating Notch1 signaling. Cell Mol Life Sci. 2023;80:223.

23. Hou X, Ren C, Jin J, et al. Phosphoinositide signalling in cell motility and adhesion. Nat Cell Biol. 2025;27:736-48.

24. Benjamim CJR, Monteiro LRL, Pontes YMM, et al. Caffeine slows heart rate autonomic recovery following strength exercise in healthy subjects. Rev Port Cardiol. 2021;40:399-406.

25. Hunter PJ, Borg TK. Integration from proteins to organs: the Physiome Project. Nat Rev Mol Cell Biol. 2003;4:237-43.

26. Zadelaar S, Kleemann R, Verschuren L, et al. Mouse models for atherosclerosis and pharmaceutical modifiers. Arterioscler Thromb Vasc Biol. 2007;27:1706-21.

27. Bowley G, Kugler E, Wilkinson R, et al. Zebrafish as a tractable model of human cardiovascular disease. Br J Pharmacol. 2022;179:900-17.

28. Taylor CA, Figueroa CA. Patient-specific modeling of cardiovascular mechanics. Annu Rev Biomed Eng. 2009;11:109-34.

29. Members WG, Hiratzka LF, Bakris GL, et al. 2010 ACCF/AHA/AATS/ACR/ASA/SCA/SCAI/SIR/STS/SVM guidelines for the diagnosis and management of patients with thoracic aortic disease: a report of the American college of cardiology foundation/American heart association task force on practice guidelines, American association for thoracic surgery, American college of radiology, American stroke association, society of cardiovascular anesthesiologists, society for cardiovascular angiography and interventions, society of interventional radiology, society of thoracic surgeons, and society for vascular medicine. Circulation. 2010;121:e266-369.

30. Yuan C, Mitsumori LM, Ferguson MS, et al. In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques. Circulation. 2001;104:2051-6.

31. Mosquera-Lopez C, Jacobs PG. Digital twins and artificial intelligence in metabolic disease research. Trends Endocrinol Metab. 2024;35:549-57.

32. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44.

33. Wang YJ, Yang K, Wen Y, et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med. 2024;30:1471-80.

34. Yu X, Chen J, Fang B, Wang W, Zhang LB, Lv Z. Cardiac LGE MRI segmentation with cross-modality image augmentation and improved U-Net. IEEE J Biomed Health Inform. 2023;27:588-97.

35. Thangaraj PM, Benson SH, Oikonomou EK, Asselbergs FW, Khera R. Cardiovascular care with digital twin technology in the era of generative artificial intelligence. Eur Heart J. 2024;45:4808-21.

36. Ali WA, Fanti MP, Roccotelli M, Ranieri L. A review of digital twin technology for electric and autonomous vehicles. Applied Sciences. 2023;13:5871.

37. Vallée A. Envisioning the future of personalized medicine: role and realities of digital twins. J Med Internet Res. 2024;26:e50204.

38. Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. Nat Comput Sci. 2024;4:184-91.

39. Kamel Boulos MN, Zhang P. Digital twins: from personalised medicine to precision public health. J Pers Med. 2021;11:745.

40. Vallée A. Digital twins for personalized medicine require epidemiological data and mathematical modeling: viewpoint. J Med Internet Res. 2025;27:e72411.

41. Guo B, Jiang M, Guo X, et al. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull. 2024;69:1472-85.

42. Chakshu NK, Sazonov I, Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomech Model Mechanobiol. 2021;20:449-65.

43. Suriani I, Bouwman RA, Mischi M, Lau KD. An in silico study of the effects of cardiovascular aging on carotid flow waveforms and indexes in a virtual population. Am J Physiol Heart Circ Physiol. 2024;326:H877-99.

44. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.

45. Boutouyrie P, Chowienczyk P, Humphrey JD, Mitchell GF. Arterial stiffness and cardiovascular risk in hypertension. Circ Res. 2021;128:864-86.

46. Wu S, Tian X, Chen S, et al. Arterial stiffness and blood pressure in treated hypertension: a longitudinal study. J Hypertens. 2023;41:768-74.

47. Islam K, Islam R, Nguyen I, et al. Diabetes mellitus and associated vascular disease: pathogenesis, complications, and evolving treatments. Adv Ther. 2025;42:2659-78.

48. Meir J, Huang L, Mahmood S, Whiteson H, Cohen S, Aronow WS. The vascular complications of diabetes: a review of their management, pathogenesis, and prevention. Expert Rev Endocrinol Metab. 2024;19:11-20.

49. Han Y, Chen K, Wang Y, et al. Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework. Nat Mach Intell. 2024;6:48-61.

50. Fu Y, Guo B, Lei Y, et al. Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography. In: Hahn HK, Mazurowski MA, Editors. Medical Imaging 2020: Computer-Aided Diagnosis; 2020 Feb 15-20; Houston, Texas, United States. SPIE, 2020;11314:1047-52.

51. Cui H, Wang Y, Li Y, et al. An improved combination of faster R-CNN and U-Net network for accurate multi-modality whole heart segmentation. IEEE J Biomed Health Inform. 2023;27:3408-19.

52. Ren K, Chang L, Wan M, Gu G, Chen Q. An improved U-net based retinal vessel image segmentation method. Heliyon. 2022;8:e11187.

53. Liu F, Zhu J, Lv B, et al. Auxiliary segmentation method of osteosarcoma MRI Image based on transformer and U-Net. Comput Intell Neurosci. 2022;2022:9990092.

54. Viedma IA, Alonso-Caneiro D, Read SA, Collins MJ. OCT retinal and choroidal layer instance segmentation using mask R-CNN. Sensors. 2022;22:2016.

55. Tiwari R, Kumar R, Malik S, Raj T, Kumar P. Analysis of heart rate variability and implication of different factors on heart rate variability. Curr Cardiol Rev. 2021;17:e160721189770.

56. Li J, Liu Z, Kong F, et al. An automatic Doppler angle analysis method for interventional blood flow velocity calibration. Ultrasonics. 2025;155:107706.

57. Abdelrahman KM, Chen MY, Dey AK, et al. Coronary computed tomography angiography from clinical uses to emerging technologies: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020;76:1226-43.

58. Yin ZX, Xu HM. An unsupervised image segmentation algorithm for coronary angiography. BioData Min. 2022;15:27.

59. Wang Y, Banerjee A, Choudhury RP, et al. DeepCA: deep learning-based 3D coronary artery tree reconstruction from two 2D non-simultaneous X-ray angiography projections. In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV); 2025 Feb 26 - 2025 Mar 6; Tucson, AZ, USA. IEEE; 2025. pp. 337-46,.

60. Bruns N. Blender: universal 3D processing and animation software. Unfallchirurg. 2020;123:747-50.

61. Kim B, Loke YH, Mass P, et al. A novel virtual reality medical image display system for group discussions of congenital heart disease: development and usability testing. JMIR Cardio. 2020;4:e20633.

62. Siddqi ZF. Computational fluid dynamics: modeling and analysis of blood flow in arteries. In: Singh RE, editor. Motion Analysis of Biological Systems. Cham: Springer International Publishing; 2024. pp. 89-121.

63. Kurniatie MD, Noviyadi NR, Mayasari DA, et al. Finite element analysis on blood vessels: understanding clipping phenomenon. In: 2024 International Seminar on Application for Technology of Information and Communication (iSemantic); 2024 Sep 21-22; Semarang, Indonesia. IEEE; 2024. pp. 107-11.

64. Syed F, Khan S, Toma M. Modeling dynamics of the cardiovascular system using fluid-structure interaction methods. Biology. 2023;12:1026.

65. Meystre S, van Stiphout R, Goris A, Gaitan S. AI-based gut-brain axis digital twins. In: Hägglund M, Blusi M, Bonacina S, Nilsson L, Cort Madsen I, Pelayo S, Moen A, Benis A, Lindsköld L, Gallos P, editors. Caring is Sharing - Exploiting the Value in Data for Health and Innovation. IOS Press; 2023. pp. 1007-8.

66. Ran X, Shi J, Chen Y, Jiang K. Multimodal neuroimage data fusion based on multikernel learning in personalized medicine. Front Pharmacol. 2022;13:947657.

67. Raissi M, Perdikaris P, Karniadakis G. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378:686-707.

68. Viceconti M, Henney A, Morley-fletcher E. In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials. 2016;3:37.

69. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56.

70. Huang K, Han Y, Chen K, et al. A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping. Nat Commun. 2021;12:2784.

71. Lyu K, Chen H, Liu Z, Zhang B, Wang R. 3D human motion prediction: a survey. Neurocomputing. 2022;489:345-65.

72. Han Y, Jiang Z, Ju F, Wang L, Liu Q, Wei P. BL-BERT: Extracting Body Language From Behavior Sequences In Freely Moving Mice. In: Liu Q, Qu Y, Wu H, Qi Y, Zeng A, Pan D, editors. Human Brain and Artificial Intelligence. Singapore: Springer Nature; 2025. pp. 181-91.

73. Wang S, Qin L. Homeostatic medicine: a strategy for exploring health and disease. Curr Med. 2022;1:16.

74. Li X, Jiang O, Chen M, Wang S. Mitochondrial homeostasis: shaping health and disease. Curr Med. 2024;3:32.

75. Li X, Cao Z, Chen M, Wang S. Redox signaling and homeostasis. Oral Sci Homeost Med. 2025;1:9610003.

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