Application and prospects of AI and new imaging technologies in the diagnosis of lower extremity arteriosclerosis obliterans_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LIN Shuangxiang ¹ , LIU Qianwen ² , QIAN Peidong ² ,  CHEN Bing ¹

1. Department of Vascular Surgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou 310009, P. R. China;
2. Boea Wisdom (Hangzhou) Network Technology Co. Ltd, Hangzhou 310000, P. R. China;

Corresponding?author：

CHEN Bing, Email: cbobc99@163.com

Keywords：

lower extremity arteriosclerosis obliterans; numerical simulation; computational fluid dynamics; biomechanics; functional assessment; precision medicine; digital twin

DOI：

10.7507/1007-9424.202512103

Video：

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Abstract Full text Figures/Tables Video References Cited by

The diagnostic and therapeutic paradigm for lower extremity arteriosclerosis obliterans (ASO) is undergoing a fundamental shift from conventional morphology-based assessment toward functional evaluation and predictive medicine. Numerical simulation techniques that integrate computational fluid dynamics (CFD) and finite element analysis (FEA), grounded in patient-specific imaging data, have emerged as a central driving force of this transformation. This review systematically elucidates how these approaches enable the construction of vascular “digital twins” to achieve precise quantification of the hemodynamic environment associated with ASO lesions, virtual monitoring of disease progression, and preoperative optimization of therapeutic strategies. Particular emphasis is placed on the critical role of numerical simulation in supporting clinical decision-making, such as evaluating the necessity of interventional treatment and predicting the mechanical responses of endovascular devices. Furthermore, the potential, current challenges, and future directions of numerical simulation in advancing personalized and precision management of ASO are comprehensively discussed.

Citation： LIN Shuangxiang, LIU Qianwen, QIAN Peidong, CHEN Bing. Application and prospects of AI and new imaging technologies in the diagnosis of lower extremity arteriosclerosis obliterans. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(1): 36-41. doi: 10.7507/1007-9424.202512103 Copy

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17.	Nematzadeh F, Zolfaghari M, Seyed-Salehi M, et al. Effect of material properties on femoral artery SMA stent performance: a numerical evaluation. Arch Appl Mech, 2025, 95(8): 189.
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19.	Lee S, Lee CW, Kim CS. FEA study on the stress distributions in the polymer coatings of cardiovascular drug-eluting stent medical devices. Ann Biomed Eng, 2014, 42(9): 1952-1965.
20.	Donik ?, Ne?emer B, Glode? S, et al. Finite element analysis of the mechanical performance of a two-layer polymer composite stent structure. Engineering Failure Analysis, 2022, 137: 106267. doi: 10.1016/j.engfailanal.2022.106267.
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1. Gimbrone MA, García-Carde?a G. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circ Res, 2016, 118(4): 620-636.
2. Storch AS, Rocha HNM, Garcia VP, et al. Oscillatory shear stress induces hemostatic imbalance in healthy men. Thromb Res, 2018, 170: 119-125.
3. Rahmati N, Pouraliakbar H, Eskandari A, et al. The impact of stenosis severity on hemodynamic parameters in the iliac artery: a fluid-structure interaction study. Bioengineering (Basel), 2025, 12(10): 1042. doi: 10.3390/bioengineering12101042.
4. Soni P, Bhattacharyya A, Usmani AY, et al. Pulsatile flow dynamics in an artery with multiple pathologies: a fluid-structure interaction study. Physics of Fluids, 2025, 37(7). doi: 10.1063/5.0263599.
5. Qiu Y, Wang J, Zhao J, et al. Association between blood flow pattern and rupture risk of abdominal aortic aneurysm based on computational fluid dynamics. Eur J Vasc Endovasc Surg, 2022, 64(2): 155-164.
6. Tan FPP, Borghi A, Mohiaddin RH, et al. Analysis of flow patterns in a patient-specific thoracic aortic aneurysm model. Computers Structures, 2009, 87(11-12): 680-690.
7. Costopoulos C, Huang Y, Brown AJ, et al. Plaque rupture in coronary atherosclerosis is associated with increased plaque structural stress. JACC Cardiovasc Imaging, 2017, 10(12): 1472-1483.
8. Conway C, McGarry JP, Edelman ER, et al. Numerical simulation of stent angioplasty with predilation: an investigation into lesion constitutive representation and calcification influence. Ann Biomed Eng, 2017, 45(9): 2244-2252.
9. Huang T, Qi X, Cao L, et al. Regional stiffness and hardening indices: new indicators derived from multidimensional dynamic CTA for aneurysm risk assessment. Adv Sci (Weinh), 2024, 11(47): e2400653. doi: 10.1002/advs.202400653.
10. Grossi B, Barati S, Ramella A, et al. Validation evidence with experimental and clinical data to establish credibility of TAVI patient-specific simulations. Comput Biol Med, 2024, 182: 109159. doi: 10.1016/j.compbiomed.2024.109159.
11. Wang Q, Kodali S, Primiano C, et al. Simulations of transcatheter aortic valve implantation: implications for aortic root rupture. Biomech Model Mechanobiol, 2015, 14(1): 29-38.
12. Schultz C, Rodriguez-Olivares R, Bosmans J, et al. Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve. EuroIntervention, 2016, 11(9): 1044-1052.
13. Avril S, Gee MW, Hemmler A, et al. Patient-specific computational modeling of endovascular aneurysm repair: state of the art and future directions. Int J Numer Method Biomed Eng, 2021, 37(12): e3529. doi: 10.1002/cnm.3529.
14. Liang S, Jia H, Zhang X, et al. In-vitro and in-silico haemodynamic analyses of a novel embedded iliac branch device. Front Cardiovasc Med, 2022, 9: 828910. doi: 10.3389/fcvm.2022.828910.
15. Ma S, Feng H, Chen Y, et al. A comparative study on the fatigue strength and service life of lower limb arterial stent at different stenosis rates. J Mech Med Biol, 2023, 23(3): 2350019.
16. Feng H, Shi X, Wang T, et al. A comparative study on the deformation behavior and mechanical properties of new lower extremity arterial stents. Comput Methods Programs Biomed, 2024, 247: 108094. doi: 10.1016/j.cmpb.2024.108094.
17. Nematzadeh F, Zolfaghari M, Seyed-Salehi M, et al. Effect of material properties on femoral artery SMA stent performance: a numerical evaluation. Arch Appl Mech, 2025, 95(8): 189.
18. Naghipoor J, Rabczuk T. A mechanistic model for drug release from PLGA-based drug eluting stent: a computational study. Comput Biol Med, 2017, 90: 15-22.
19. Lee S, Lee CW, Kim CS. FEA study on the stress distributions in the polymer coatings of cardiovascular drug-eluting stent medical devices. Ann Biomed Eng, 2014, 42(9): 1952-1965.
20. Donik ?, Ne?emer B, Glode? S, et al. Finite element analysis of the mechanical performance of a two-layer polymer composite stent structure. Engineering Failure Analysis, 2022, 137: 106267. doi: 10.1016/j.engfailanal.2022.106267.
21. Michard F, Mulder MP, Gonzalez F, et al. AI for the hemodynamic assessment of critically ill and surgical patients: focus on clinical applications. Ann Intensive Care, 2025, 15(1): 26. doi: 10.1186/s13613-025-01448-w.
22. Buckler AJ, Karl?f E, Lengquist M, et al. Virtual transcriptomics: noninvasive phenotyping of atherosclerosis by decoding plaque biology from computed tomography angiography imaging. Arterioscler Thromb Vasc Biol, 2021, 41(5): 1738-1750.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Application and prospects of AI and new imaging technologies in the diagnosis of lower extremity arteriosclerosis obliterans

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