基于力学参数的血管介入机器人血管角度实时预测与安全预警策略初步研究

doi:10.3969/j.issn.1674-8115.2026.03.001

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (3): 265-274.doi: 10.3969/j.issn.1674-8115.2026.03.001

• 论著 · 基础研究 •

基于力学参数的血管介入机器人血管角度实时预测与安全预警策略初步研究

贾远望¹^,², 屈铭³, 辛然⁴^,¹, 刘子暖¹, 杨诗怡⁵, 康文³, 王蔚然¹, 刘肖⁵, 杨俊杰¹(), 陈韵岱¹()

^1.中国人民解放军总医院第六医学中心心血管病医学部，北京 100048
^2.中国人民解放军总医院研究生院，北京 100853
^3.北京理工大学数学与统计学院，北京 100081
^4.南开大学医学院，天津 300071
^5.北京航空航天大学生物与医学工程学院，北京 100083

收稿日期:2025-07-27 接受日期:2025-11-21 出版日期:2026-03-28 发布日期:2026-03-30
通讯作者: 陈韵岱，主任医师，博士；电子信箱：cyundai@vip.163.com
杨俊杰，副主任医师，博士；电子信箱：fearlessyang@126.com。^#为共用通信作者。
基金资助:
国家自然科学基金(82470342,12361141817,62273046);北京市科技新星计划(20230484471)

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

Jia Yuanwang¹^,², Qu Ming³, Xin Ran⁴^,¹, Liu Zinuan¹, Yang Shiyi⁵, Kang Wen³, Wang Weiran¹, Liu Xiao⁵, Yang Junjie¹(), Chen Yundai¹()

^1.Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital, Beijing 100048, China
^2.Medical School of Chinese PLA, Beijing 100853, China
^3.School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
^4.School of Medicine, Nankai University, Tianjin 300071, China
^5.School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China

Received:2025-07-27 Accepted:2025-11-21 Online:2026-03-28 Published:2026-03-30
Contact: Chen Yundai, E-mail: cyundai@vip.163.com
Yang Junjie, E-mail: fearlessyang@126.com.
Supported by:
National Natural Science Foundation of China(82470342,12361141817,62273046);Beijing Nova Program(20230484471)

摘要/Abstract

摘要：

目的·聚焦血管介入机器人多模态感知缺失与X射线辐射依赖的问题，探究不同血管弯曲角度下导丝与血管壁之间接触力学特征，通过构建接触力-角度映射模型，初步提出基于力学参数的血管角度预测与安全预警融合的血管介入机器人的新辅助策略。方法·在体外血管模型（弯曲角度梯度：0°~80°，间隔5°）中，采用R-One机器人平台以恒定速度（6 mm/s）推送力反馈导丝。通过时间配准策略，提取导丝通过血管弯曲段初期（2 s时间窗）的接触力变化趋势特征（即接触力变化速率），并量化血管弯曲角度对该特征动态变化的影响规律。基于此构建接触力变化趋势特征与角度映射模型，并采用均方根误差（root mean squared error，RMSE）和平均绝对误差（mean absolute error，MAE）评估模型性能。结果·血管弯曲角度与导丝通过弯曲段初期的接触力变化速率呈极强正相关（r_s =0.98，P<0.001），且该关系呈现明显阶段性（与0°水平基准比较）：在0°~25°范围内，接触力变化速率保持稳定，无统计学显著差异；从30°起首次出现显著上升（修正后P<0.05）；而在50°之后，增幅呈现加速趋势，统计显著性也同步增强（修正后P从10⁻⁵降至10⁻⁸量级），直至80°时接触力变化率增幅达到24 392.4%；贝叶斯信息准则（Bayesian information criterion，BIC）变点分析识别出35.60°与50.65°等关键变点，与基于统计学判断的30°与50°经验性拐点高度吻合，进一步验证了该关系的结构性特征。基于上述关系构建接触力-角度映射模型，此模型表现优异（R²=0.96，RMSE=4.93°，MAE=3.73°），显著优于传统线性模型（R²=0.89，RMSE=7.66°，MAE=5.03°）。结论·在体外模型中，导丝入弯初期的接触力变化速率与血管弯曲角度呈显著正相关且存在阶段性变化与关键拐点，基于该特征建立了优于传统线性模型的接触力-角度映射模型。初步证实了利用力学特征推断血管解剖结构的可行性，为实现基于力学特征的安全预警与辅助导航提供了初步理论依据。

关键词: 血管介入机器人, 力反馈, 接触力变化速率, 血管角度, 安全预警与导航

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

中图分类号:

R318.6

贾远望, 屈铭, 辛然, 刘子暖, 杨诗怡, 康文, 王蔚然, 刘肖, 杨俊杰, 陈韵岱. 基于力学参数的血管介入机器人血管角度实时预测与安全预警策略初步研究[J]. 上海交通大学学报（医学版）, 2026, 46(3): 265-274.

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.

图/表 9

图1 J-型血管模型示意图

Fig 1 Schematic diagram of the J-shaped vascular model

图2 力反馈导丝示意图

Fig 2 Schematic diagram of the force-feedback guidewire

图3 力反馈导丝行进路径可视化与接触力动态响应联合分析图

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

图4 血管弯曲角度与接触力变化速率的关系散点图及BIC变点检测Note： To identify structural change points in the angle-contact force relationship, this study automatically detects change points based on the Bayesian Information criterion (BIC). The graphical results confirm the presence of multiple phase transitions in this relationship.

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

图5 不同血管弯曲角度之间导丝-血管壁接触力变化率的相对变化率热图Note： This heatmap quantitatively illustrates the percentage difference in the rate of change of contact force when the force-sensing guidewire transitions from an initial vascular bending angle (x-axis) to a target angle (y-axis). Color intensity indicates the magnitude of change, with darker hues representing greater variation gradients.

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

表1 不同血管弯曲角度下接触力变化速率的分布特征

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)

图6 基于导丝-血管壁接触力的血管角度预测模型拟合曲线Note： This graph depicts the relationship between the rate of change in guidewire contact force (x-axis) and vascular bending angle (y-axis), showing raw data as scatter points and the fitted curve with its 95% confidence interval as a shaded band.

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

表2 模型性能比较

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

图7 血管角度预测模型验证：真实值与预测值的散点分布

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

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基于力学参数的血管介入机器人血管角度实时预测与安全预警策略初步研究

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

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