Citation: Wei, W.; Yang, D.; Li, L.; Xia,
Y. An Intravascular Catheter Bending
Recognition Method for
Interventional Surgical Robots.
Machines 2022, 10, 42. https://
doi.org/10.3390/machines10010042
Academic Editors: Xiaochun Cheng
and Daming Shi
Received: 23 November 2021
Accepted: 3 January 2022
Published: 6 January 2022
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Article
An Intravascular Catheter Bending Recognition Method for
Interventional Surgical Robots
Wei Wei *, Dong Yang, Li Li and Yuxuan Xia
School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215131, China;
20204239033@stu.suda.edu.cn (D.Y.); lli1995@stu.suda.edu.cn (L.L.); 20214239034@stu.suda.edu.cn (Y.X.)
* Correspondence: weiwei0728@suda.edu.cn; Tel.: +86-180-1316-3567
Abstract:
Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons
during the operation, but the lack of force detection and force feedback is still a risk in the operation
which may harm the patient. In those robotic surgeries, the traditional force detection methods may
have measurement losses and errors caused by mechanical transmission and cannot identify the
direction of the force. In this paper, an interventional surgery robot system with a force detection
device is designed and a new force detection method based on strain gauges is proposed to detect
the force and infer the bending direction of the catheter in the vessel by using BP neural network. In
addition, genetic algorithm is used to optimize the BP neural network, and the error between the
calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter
bending recognition. Combining this new method with traditional force sensors not only reduces the
error caused by the traditional mechanical transmission, but also can detect the bending direction of
the catheter in the blood vessel, which greatly improves the safety of the operation.
Keywords: vascular interventional surgery; force detection; robot-assisted surgery; strain gauges
1. Introduction
Cardiovascular and cerebrovascular diseases are some of the most serious diseases
that threaten human life and health. According to statistics, the number of deaths from
cardiovascular and cerebrovascular diseases in developed countries accounts for 34% of
the annual deaths [
1
]. At present, vascular interventional surgery is the best treatment for
vascular diseases such as thrombosis, vascular sclerosis and vasoconstriction. The doctor
sends the medical guidewire and catheter from the patient’s radial artery into the human
blood vessel and pushes them to the blood vessel where the lesion is located for treatment.
The surgical incision is small and the treatment effect is good, which is widely recognized
in the world [
2
]. In the traditional operation process, the digital subtraction angiography
system (DSA) is needed for positioning, even if wearing heavy metal protective clothing,
the surgeons operating the catheter will still be injured by radiation [
3
]. Surgeons also
need skills and experience to carry out the operation, which increases the difficulty of
the operation. In traditional surgery, the surgeons must stand beside the patient and
position the catheter and guidewire to the target location under the guidance of the digital
subtraction angiography (DSA) system. This process often lasts for several hours, which
may cause fatigue and tremor of the surgeon’s hands, thereby affecting the success of
the operation and even threatening the life of the patient. Therefore, researchers around
the world are increasingly interested in vascular interventional surgery robots which can
perform remote surgery to reduce the fatigue and physical harm to the surgeons.
In recent years, the research on interventional surgical robots has increased year by
year [
4
]. There are many mature commercial surgical robot systems. In 2002, Steracoaxis Inc,
St. Louis, MO, USA, developed the NIOBE remote navigation system which can navigate
the catheter by a magnetic field [
5
]. In 2005, the CorPath 200 robot system was developed
Machines 2022, 10, 42. https://doi.org/10.3390/machines10010042 https://www.mdpi.com/journal/machines
