3D打印技术、扩展现实与人工智能在结构性心脏病经导管介入治疗中的应用进展

doi:10.16138/j.1673-6087.2026.01.12

摘要/Abstract

摘要：

随着微创介入技术的进步，结构性心脏病（structural heart disease, SHD）经导管介入治疗迅速发展。然而，由于心脏解剖结构的复杂性，SHD经导管介入治疗对术前规划、术中导航及术后评估提出了更高的要求。传统二维影像学技术在处理复杂SHD时存在局限性。3D打印、扩展现实（extended reality, XR）及人工智能（artificial intelligence, AI）等技术的应用，为SHD介入治疗提供更准确的技术支持。本文就上述技术在SHD经导管介入治疗领域中的最新应用进展，包括术前规划、手术模拟、术中导航和术后评估等关键环节进行综述，以期为心血管介入医师应用新兴技术优化诊疗策略提供参考。

关键词: 结构性心脏病, 经导管介入治疗, 3D打印技术, 扩展现实, 人工智能

Abstract:

With the advancement of minimally invasive interventional technology, transcatheter intervention for the treatment of structural heart disease (SHD) has developed rapidly. However, due to the complexity of the heart's anatomical structure, the transcatheter intervention treatment for SHD imposes higher demands on preoperative planning, intraoperative navigation, and postoperative assessment. Traditional two-dimensional imaging techniques have limitations when dealing with complex SHD. The application of technologies such as 3D printing, extended reality (XR), and artificial intelligence (AI) provides more accurate technical support for transcatheter interventional treatment of SHD. This article reviews the latest advances in the application progress of these three technologies in SHD transcatheter intervention, including key aspects such as preoperative planning, surgical simulation, intraoperative navigation, and postoperative assessment, with the aim of providing a reference for cardiovascular interventional physicians to optimize diagnosis and treatment strategies for SHD by applying emerging technologies.

Key words: structural heart disease, transcatheter intervention, 3D printing technology, extended reality, artificial intelligence

中图分类号:

R541.2

练春燕, 轩辕东, 李晓群. 3D打印技术、扩展现实与人工智能在结构性心脏病经导管介入治疗中的应用进展[J]. 内科理论与实践, 2026, 21(01): 80-84.

LIAN Chunyan, XUANYUAN Dong, LI Xiaoqun. Advances in the application of 3D printing technology, extended reality, and artificial intelligence in the transcatheter percutaneous intervention for structural heart disease[J]. Journal of Internal Medicine Concepts & Practice, 2026, 21(01): 80-84.

图/表 2

参考文献 33

[1]	葛均波. 结构性心脏病的定义、范畴及其现状和未来[J]. 上海医学, 2021, 44(4):217-220. doi: 10.19842/j.cnki.issn.0253-9934.2021.04.001
	Ge JB. Definition, scope, current status, and future directions of structural heart disease[J]. Shanghai Med J, 2021, 44(4):217-220. doi: 10.19842/j.cnki.issn.0253-9934.2021.04.001
[2]	Sun Z, Zhao J, Leung E, et al. Three-dimensional bioprinting in cardiovascular disease: current status and future directions[J]. Biomolecules, 2023, 13(8):1180. doi: 10.3390/biom13081180
[3]	Isikay I, Cekic E, Baylarov B, et al. Narrative review of patient-specific 3D visualization and reality technologies in skull base neurosurgery: enhancements in surgical training, planning, and navigation[J]. Front Surg, 2024, 11:1427844. doi: 10.3389/fsurg.2024.1427844
[4]	Samant S, Bakhos JJ, Wu W, et al. Artificial intelligence, computational simulations, and extended reality in cardiovascular interventions[J]. JACC Cardiovasc Interv, 2023, 16(20):2479-2497. doi: 10.1016/j.jcin.2023.07.022
[5]	Echarte-Morales J, Toribio-García I, Diéguez AR, et al. Applications of three-dimensional printing in percutaneous closure of aortic-to-right ventricle fistula after transcatheter aortic valve replacement[J]. Eur Heart J Case Rep, 2024, 8(5):ytae112. doi: 10.1093/ehjcr/ytae112
[6]	Mao Y, Liu Y, Zhai M, et al. Application of and prospects for 3-dimensional printing in transcatheter mitral valve interventions[J]. Rev Cardiovasc Med, 2023, 24(2):61. doi: 10.31083/j.rcm2402061
[7]	Qian Z, Wang K, Liu S, et al. Quantitative prediction of paravalvular leak in transcatheter aortic valve replacement based on tissue-mimicking 3D printing[J]. JACC Cardiovasc Imaging, 2017, 10(7):719-731. doi: 10.1016/j.jcmg.2017.04.005
[8]	Annabestani M, Sriram S, Caprio A, et al. High-fidelity pose estimation for real-time extended reality (XR) visualization for cardiac catheterization[J]. Sci Rep, 2024, 14(1):26962. doi: 10.1038/s41598-024-76384-z
[9]	Vegulla RV, Greil G, Reddy SV, et al. Biplane 3D overlay guidance for congenital heart disease to assist cardiac catheterization interventions - a pilot study[J]. JRSM Cardiovasc Dis, 2024, 13:20480040241274521.
[10]	Mao Y, Zhu G, Zhai M, et al. Transcatheter aortic valve replacement and coronary protection guided by deep learning and 3-dimensional printing[J]. Surg Innov, 2024, 31(3):256-262. doi: 10.1177/15533506241244571
[11]	Zhao X, Eren OC, Molyneux A, et al. Development of a methodology for in vitro and in silico simulation of transcatheter aortic valve replacement using 3D-printed valve frames[J]. Comput Biol Med 2025, 186:109690. doi: 10.1016/j.compbiomed.2025.109690
[12]	Faza NN, Harb SC, Wang DD, et al. Physical and computational modeling for transcatheter structural heart interventions[J]. JACC Cardiovasc Imaging, 2024, 17(4):428-440. doi: 10.1016/j.jcmg.2024.01.014
[13]	Sun Z, Wee C. 3D printed models in cardiovascular disease: an exciting future to deliver personalized medicine[J]. Micromachines (Basel), 2022, 13(10):1575. doi: 10.3390/mi13101575
[14]	Mao Y, Liu Y, Ma Y, et al. Feasibility of 3-dimensional printed models in simulated training and teaching of transcatheter aortic valve replacement[J]. Open Med (Wars), 2024, 19(1):20240909. doi: 10.1515/med-2024-0909
[15]	Sun Z, Lau I, Wong YH, et al. Personalized three-dimensional printed models in congenital heart disease[J]. J Clin Med, 2019, 8(4):522
[16]	Huang H, Wu Y. A deep learning-based method for rapid 3D whole-heart modeling in congenital heart disease[J]. Cardiology, 2025, 150(3):243-258. doi: 10.1159/000542318
[17]	Raimondi F, Vida V, Godard C, et al. Fast-track virtual reality for cardiac imaging in congenital heart disease[J]. J Card Surg, 2021, 36(7):2598-2602. doi: 10.1111/jocs.15508
[18]	Gehrsitz P, Rompel O, Schöber M, et al. Cinematic rendering in mixed-reality holograms: a new 3D preoperative planning tool in pediatric heart surgery[J]. Front Cardiovasc Med, 2021, 8:633611. doi: 10.3389/fcvm.2021.633611
[19]	Lau I, Gupta A, Ihdayhid A, et al. Clinical applications of mixed reality and 3D printing in congenital heart disease[J]. Biomolecules, 2022, 12(11):1548. doi: 10.3390/biom12111548
[20]	Stephenson N, Pushparajah K, Wheeler G, et al. Extended reality for procedural planning and guidance in structural heart disease - a review of the state-of-the-art[J]. Int J Cardiovasc Imaging, 2023, 39(7):1405-1419. doi: 10.1007/s10554-023-02823-z
[21]	Keramati H, Lu X, Cabanag M, et al. Applications and advances of immersive technology in cardiology[J]. Curr Probl Cardiol, 2024, 49(10):102762. doi: 10.1016/j.cpcardiol.2024.102762
[22]	Stepanenko A, Perez LM, Ferre JC, et al. 3D virtual modelling, 3D printing and extended reality for planning of implant procedure of short-term and long-term mechanical circulatory support devices and heart transplantation[J]. Front Cardiovasc Med, 2023, 10:1191705. doi: 10.3389/fcvm.2023.1191705
[23]	Bavo AM, Wilkins BT, Garot P, et al. Validation of a computational model aiming to optimize preprocedural planning in percutaneous left atrial appendage closure[J]. J Cardiovasc Comput Tomogr, 2020, 14(2):149-154. doi: 10.1016/j.jcct.2019.08.010
[24]	Patel H, Choi P, Ku JC, et al. Application of three-dimensional printing in the planning and execution of aortic aneurysm repair[J]. Front Cardiovasc Med, 2025, 11:1485267. doi: 10.3389/fcvm.2024.1485267
[25]	Hernandez-Suarez DF, Kim Y, Villablanca P, et al. Machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement[J]. JACC Cardiovasc Interv, 2019, 12(14):1328-1338. doi: 10.1016/j.jcin.2019.06.013
[26]	Hernandez-Suarez DF, Ranka S, Kim Y, et al. Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States[J]. Cardiovasc Revasc Med, 2021, 22:22-28. doi: 10.1016/j.carrev.2020.06.017
[27]	Ma L, Yu S, Xu X, et al. Application of artificial intelligence in 3D printing physical organ models[J]. Mater Today Bio, 2023, 23:100792. doi: 10.1016/j.mtbio.2023.100792
[28]	Androshchuk V, Montarello N, Lahoti N, et al. Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions - new opportunities for precision medicine[J]. Int J Cardiovasc Imaging, 2026, 42(3):483-501. doi: 10.1007/s10554-024-03247-z
[29]	Sarkar K, Bhimarasetty V, Rahim A, et al. Assessing the feasibility and utility of patient-specific 3D advanced visualization modeling in cerebrovascular disease: retrospective analysis and prospective survey pilot study[J]. JMIR Form Res, 2025, 9:e51939. doi: 10.2196/51939
[30]	Lu A, Williams RO 3rd, Maniruzzaman M. 3D printing of biologics - what has been accomplished to date?[J]. Drug Discov Today, 2024, 29(1):103823. doi: 10.1016/j.drudis.2023.103823
[31]	Kabirian F, Mela P, Heying R. 4D printing applications in the development of smart cardiovascular implants[J]. Front Bioeng Biotechnol, 2022, 10:873453. doi: 10.3389/fbioe.2022.873453
[32]	Bhandari S, Yadav V, Ishaq A, et al. Trends and challenges in the development of 3D-printed heart valves and other cardiac implants: a review of current advances[J]. Cureus, 2023, 15(8):e43204. doi: 10.7759/cureus.43204
[33]	Biswas AA, Dhondale MR, Agrawal AK, et al. Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology[J]. Drug Deliv Transl Res, 2024, 14(6):1458-1479. doi: 10.1007/s13346-023-01510-9