Preliminary study on a mechanics-parameter-based strategy for real-time vascular angle prediction and safety warning in vascular interventional robots

doi:10.3969/j.issn.1674-8115.2026.03.001

Abstract

Abstract:

Objective ·This study addresses the challenges of multimodal perception deficiencies and X-ray radiation dependency in vascular interventional robots. It investigated the mechanical characteristics of guidewire-vessel wall contact forces across varying vascular bending angles and established a contact force-angle mapping model to develop a novel robotic-assisted strategy integrating real-time vascular angle prediction and safety warning based on mechanical parameters. Methods ·An in vitro vascular model (bending angles: 0°‒80° at 5° intervals) was deployed on the R-One robotic platform. A force-sensing guidewire was advanced at 6 mm/s through curved segments. Using temporal registration, dynamic contact force variation trends (quantified as the rate of change) were extracted during the initial 2s traversal window. The correlation between vascular bending angles and variation rates was quantified. Based on this, a mapping model between contact force trends and vascular angles was constructed and evaluated using root mean squared error (RMSE) and mean absolute error (MAE). Results ·A very strong positive correlation was observed between vascular bending angle and the rate of change in guidewire contact force during the initial phase of traversal through the bend (r_s =0.98, P<0.001). This relationship exhibited distinct phases relative to the 0° baseline: the rate remained stable within the 0°‒25°, with no statistically significant differences; a significant increase first appeared at 30° (P.adj<0.05); beyond 50°, the rate of increase accelerated markedly, accompanied by a sharp enhancement in statistical significance (P.adj decreasing from 10^-5 to 10^-8). By 80°, the rate of change in contact force increased by 24, 392.4%. Bayesian Information Criterion (BIC)-based changepoint analysis identified critical transition points at 35.60° and 50.65°, which closely align with the empirical thresholds of 30° and 50°, further confirming the structural nature of the relationship. The contact force-angle mapping model developed based on this relationship demonstrated excellent performance (R²=0.96, RMSE=4.93°, MAE=3.73°), significantly outperforming a conventional linear model (R²=0.89, RMSE=7.66°, MAE=5.03°). Conclusion ·In the in vitro model, the rate of change of contact force during the initial phase of guidewire entry into a curved segment exhibited a significant positive correlation with vascular bending angle, characterized by distinct phase transitions and critical inflection points. Based on this feature, a contact force-angle mapping model was established, which outperformed the traditional linear model. This study preliminarily validates the feasibility of inferring vascular anatomical structures from mechanical characteristics, providing a theoretical basis for mechanics-based safety warning and auxiliary navigation.

Key words: vascular interventional robot, force feedback, rate of change of contact force, vascular curvature, safety warning and navigation

CLC Number:

R318.6

Jia Yuanwang, Qu Ming, Xin Ran, Liu Zinuan, Yang Shiyi, Kang Wen, Wang Weiran, Liu Xiao, Yang Junjie, Chen Yundai. Preliminary study on a mechanics-parameter-based strategy for real-time vascular angle prediction and safety warning in vascular interventional robots[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(3): 265-274.

Figures/Tables 9

Fig 1 Schematic diagram of the J-shaped vascular model

Fig 2 Schematic diagram of the force-feedback guidewire

Fig 3 Integrated analysis of force-feedback guidewire trajectory visualization and contact force dynamic response

Fig 4 Scatter plot of the rate of change of guidewire contact force versus vascular bending angle with Bayesian information criterion (BIC)-based change point detection

Fig 5 Heatmap of relative percentage changes in the rate of change of guidewire-vessel wall contact force across vascular bending angles

Tab 1 Distribution characteristics of the rate of change of guidewire-vessel wall contact force across vascular bending angles

Angle/°	Rate of change of contact force (n=340)
0	0.000 04 (0.000 01, 0.000 06)
5	0.000 13 (0.000 10, 0.000 17)
10	0.000 14 (0.000 13, 0.000 15)
15	0.000 21 (0.000 18, 0.000 24)
20	0.000 22 (0.000 18, 0.000 26)
25	0.000 24 (0.000 22, 0.000 27)
30	0.000 32 (0.000 30, 0.000 36)
35	0.000 34 (0.000 25, 0.000 43)
40	0.000 46 (0.000 36, 0.000 52)
45	0.000 54 (0.000 44, 0.000 57)
50	0.000 68 (0.000 63, 0.000 73)
55	0.000 88 (0.000 81, 0.000 96)
60	0.001 22 (0.001 07, 0.001 32)
65	0.001 47 (0.001 20, 0.001 65)
70	0.002 00 (0.001 94, 0.002 08)
75	0.002 72 (0.002 38, 0.002 87)
80	0.003 76 (0.003 45, 0.003 92)

Fig 6 Nonparametric fitting curve of vascular angle prediction model based on guidewire-vessel wall contact force

Tab2 Comparative analysis of model predictive performance

	Baseline linear model (95% CI)	The proposed polynomial regression model (95% CI)	P
R²	0.89 (0.83, 0.93)	0.96 (0.94, 0.97)	<0.001
RMSE/°	7.66 (6.07, 9.80)	4.93 (4.17, 5.75)	0.009
MAE/°	5.03 (4.67, 6.82)	3.73 (3.09, 4.37)	<0.001

Fig 7 Validation of vascular angle prediction model: scatter distribution of ground-truth and predicted values

TrendMD

References 24

[1]	Virani S S, Alonso A, Aparicio H J, et al. Heart disease and stroke statistics-2021 update: a report from the American heart association[J]. Circulation, 2021, 143(8): e254-e743.
[2]	Lee M S, Flammer A J, Kim H S, et al. The prevalence of cardiovascular disease risk factors and the Framingham risk score in patients undergoing percutaneous intervention over the last 17 years by gender: time-trend analysis from the mayo clinic PCI registry[J]. J Prev Med Public Health, 2014, 47(4): 216-229.
[3]	Sernia S, Bongiovanni A, de Giorgi A, et al. Thyroid parameters variations in healthcare workers and students exposed to low-dose ionizing radiations[J]. G Ital Med Lav Ergon, 2022, 44(3): 338-346.
[4]	Liu J Y, Bi K, Yang R, et al. Role of DNA damage and repair in radiation cancer therapy: a current update and a look to the future[J]. Int J Radiat Biol, 2020, 96(11): 1329-1338.
[5]	Durand E, Sabatier R, Smits P C, et al. Evaluation of the R-One robotic system for percutaneous coronary intervention: the R-EVOLUTION study[J]. EuroIntervention, 2023, 18(16): e1339-e1347.
[6]	Meng C, Zhang J, Liu D, et al. A remote-controlled vascular interventional robot: system structure and image guidance[J]. Int J Med Robot Comput Assist Surg, 2013, 9(2): 230-239.
[7]	Guo J, Guo S X, Shao L, et al. Design and performance evaluation of a novel robotic catheter system for vascular interventional surgery[J]. Microsyst Technol, 2016, 22(9): 2167-2176.
[8]	Shi C C, Ishihara H. Performance evaluation of a vascular interventional surgery robotic system with visual-based force feedback[J]. Machines, 2023, 11(7): 727.
[9]	Adler Y, Ristić A D, Imazio M, et al. Cardiac tamponade[J]. Nat Rev Dis Primers, 2023, 9: 36.
[10]	Fejka M, Dixon S R, Safian R D, et al. Diagnosis, management, and clinical outcome of cardiac tamponade complicating percutaneous coronary intervention[J]. Am J Cardiol, 2002, 90(11): 1183-1186.
[11]	Tao L, Xie H Z, Zhang S Y, et al. Safety protection based on electromagnetic navigation in robot-assisted vascular interventional surgery[C]//2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). October 14-16, 2017, Shanghai, China. IEEE, 2018: 1-5.
[12]	Yu H Y, Wang H B, Chang J Y, et al. Design and evaluation of vascular interventional robot system for complex coronary artery lesions[J]. Med Biol Eng Comput, 2023, 61(6): 1365-1380.
[13]	Guo J, Jin X L, Guo S X. Study of the operational safety of a vascular interventional surgical robotic system[J]. Micromachines, 2018, 9(3): 119.
[14]	Fang Y X, Yang F, He W, et al. Intravascular catheter navigation and thrombus localization using hybrid electrodes electric field stereotaxis[J]. Measurement, 2025, 250: 117169.
[15]	Yang C, Guo S X, Bao X Q, et al. A vascular interventional surgical robot based on surgeon's operating skills[J]. Med Biol Eng Comput, 2019, 57(9): 1999-2010.
[16]	Mussmann B, Larsen T R, Godballe M, et al. Radiation dose to multidisciplinary staff members during complex interventional procedures[J]. Radiography (Lond), 2024, 30(2): 512-516.
[17]	Song Y, Li L T, Tian Y, et al. A novel master-slave interventional surgery robot with force feedback and collaborative operation[J]. Sensors (Basel), 2023, 23(7): 3584.
[18]	周兆熊, 袁键键, 邵华, 等. 血管介入治疗导丝推进力与血管阻塞体强度及长度特征关系试验研究[J]. 中国测试, 2019, 45(7): 31-36.
	Zhou Z X, Yuan J J, Shao H, et al. Experimental study on the relationship between the force of guide wire in mechanically assisted vascular intervention and the strength and length of vascular obstruction[J]. China Measurement & Test, 2019, 45(7): 31-36.
[19]	Li P, Feng J, Zhang X, et al. Modeling and experimental study of the intervention forces between the guidewire and blood vessels[J]. Med Eng Phys, 2024, 127: 104166.
[20]	Rafii-Tari H, Riga C V, Payne C J, et al. Reducing contact forces in the arch and supra-aortic vessels using the Magellan robot[J]. J Vasc Surg, 2016, 64(5): 1422-1432.
[21]	任龙飞, 孟偲, 刘博, 等. 基于图像的介入器械前端变形建模与受力估计[J]. 中国图象图形学报, 2022, 27(3): 1001-1008.
	Ren L F, Meng C, Liu B, et al. Image-based deformation modeling and force estimation of the front end of vascular interventional surgical apparatus[J]. Journal of Image and Graphics, 2022, 27(3): 1001-1008.
[22]	Yan Y G, Guo S X, Lin Z J, et al. Grasping-force-based passive safety method for a vascular interventional surgery robot system[J]. IEEE Trans Instrum Meas, 2023, 72: 7506412.
[23]	Song J W, Yang K K, Chen H, et al. VascularPilot3D: toward a 3D fully autonomous navigation for endovascular robotics[C]//2025 IEEE International Conference on Robotics and Automation (ICRA). May 19-23, 2025. Atlanta, GA, USA. IEEE, 2025: 9318-9324.
[24]	Yang D, Xiao N, Xia Y X, et al. An interventional surgical robot based on multi-data detection[J]. Appl Sci, 2023, 13(9): 5301.