Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot
Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Fibers |
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| Online Access: | https://www.mdpi.com/2079-6439/10/12/106 |
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| author | Xingyu Chen Yinan Chen Wenke Duan Toluwanimi Oluwadara Akinyemi Guanlin Yi Jie Jiang Wenjing Du Olatunji Mumini Omisore |
| author_facet | Xingyu Chen Yinan Chen Wenke Duan Toluwanimi Oluwadara Akinyemi Guanlin Yi Jie Jiang Wenjing Du Olatunji Mumini Omisore |
| author_sort | Xingyu Chen |
| collection | DOAJ |
| description | Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities. |
| first_indexed | 2024-03-09T16:46:20Z |
| format | Article |
| id | doaj.art-d76e7fe5ec104bee9e8be92d7fc5c61b |
| institution | Directory Open Access Journal |
| issn | 2079-6439 |
| language | English |
| last_indexed | 2024-03-09T16:46:20Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fibers |
| spelling | doaj.art-d76e7fe5ec104bee9e8be92d7fc5c61b2023-11-24T14:46:25ZengMDPI AGFibers2079-64392022-12-01101210610.3390/fib10120106Design and Evaluation of a Learning-Based Vascular Interventional Surgery RobotXingyu Chen0Yinan Chen1Wenke Duan2Toluwanimi Oluwadara Akinyemi3Guanlin Yi4Jie Jiang5Wenjing Du6Olatunji Mumini Omisore7Research Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaResearch Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaInterventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.https://www.mdpi.com/2079-6439/10/12/106vascular interventional surgical robotforce feedbackendovascular catheterizationlearning-based models |
| spellingShingle | Xingyu Chen Yinan Chen Wenke Duan Toluwanimi Oluwadara Akinyemi Guanlin Yi Jie Jiang Wenjing Du Olatunji Mumini Omisore Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot Fibers vascular interventional surgical robot force feedback endovascular catheterization learning-based models |
| title | Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot |
| title_full | Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot |
| title_fullStr | Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot |
| title_full_unstemmed | Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot |
| title_short | Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot |
| title_sort | design and evaluation of a learning based vascular interventional surgery robot |
| topic | vascular interventional surgical robot force feedback endovascular catheterization learning-based models |
| url | https://www.mdpi.com/2079-6439/10/12/106 |
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