Design of a robot-assisted system for transforaminal percutaneous endoscopic lumbar surgeries: study protocol.


Journal

Journal of orthopaedic surgery and research
ISSN: 1749-799X
Titre abrégé: J Orthop Surg Res
Pays: England
ID NLM: 101265112

Informations de publication

Date de publication:
19 Oct 2020
Historique:
received: 14 07 2020
accepted: 06 10 2020
entrez: 20 10 2020
pubmed: 21 10 2020
medline: 15 5 2021
Statut: epublish

Résumé

Transforaminal percutaneous endoscopic lumbar surgeries (PELS) for lumbar disc herniation and spinal stenosis are growing in popularity. However, there are some problems in the establishment of the working channel and foraminoplasty such as nerve and blood vessel injuries, more radiation exposure, and steeper learning curve. Rapid technological advancements have allowed robotic technology to assist surgeons in improving the accuracy and safety of surgeries. Therefore, the purpose of this study is to develop a robot-assisted system for transforaminal PELS, which can provide navigation and foraminoplasty. The robot-assisted system consists of three systems: preoperative planning system, navigation system, and foraminoplasty system. In the preoperative planning system, 3D visualization of the surgical segment and surrounding tissues are realized using the multimodal image fusion technique of computed tomography and magnetic resonance imaging, and the working channel planning is carried out to reduce the risk for injury to vital blood vessels and nerves. In the navigation system, the robot can obtain visual perception ability from a visual receptor and automatically adjust the robotic platform and robot arm to the appropriate positions according to the patient's position and preoperative plan. In addition, the robot can automatically register the surgical levels through intraoperative fluoroscopy. After that, the robot will provide navigation using the 6 degree-of-freedom (DOF) robot arm according to the preoperative planning system and guide the surgeon to complete the establishment of the working channel. In the foraminoplasty system, according to the foraminoplasty planning in the preoperative planning system, the robot performs foraminoplasty automatically using the high speed burr at the end of the robot arm. The system can provide real-time feedback on the working status of the bur through multi-mode sensors such as multidimensional force, position, and acceleration. Finally, a prototype of the system is constructed and performance tests are conducted. Our study will develop a robot-assisted system to perform transforaminal PELS, and this robot-assisted system can also be used for other percutaneous endoscopic spinal surgeries such as interlaminar PELS and percutaneous endoscopic cervical and thoracic surgeries through further research. The development of this robot-assisted system can be of great significance. First, the robot can improve the accuracy and efficiency of endoscopic spinal surgeries. In addition, it can avoid multiple intraoperative fluoroscopies, minimize exposure to both patients and the surgical staff, shorten the operative time, and improve the learning curve of beginners, which is beneficial to the popularization of percutaneous endoscopic spinal surgeries.

Sections du résumé

BACKGROUND BACKGROUND
Transforaminal percutaneous endoscopic lumbar surgeries (PELS) for lumbar disc herniation and spinal stenosis are growing in popularity. However, there are some problems in the establishment of the working channel and foraminoplasty such as nerve and blood vessel injuries, more radiation exposure, and steeper learning curve. Rapid technological advancements have allowed robotic technology to assist surgeons in improving the accuracy and safety of surgeries. Therefore, the purpose of this study is to develop a robot-assisted system for transforaminal PELS, which can provide navigation and foraminoplasty.
METHODS METHODS
The robot-assisted system consists of three systems: preoperative planning system, navigation system, and foraminoplasty system. In the preoperative planning system, 3D visualization of the surgical segment and surrounding tissues are realized using the multimodal image fusion technique of computed tomography and magnetic resonance imaging, and the working channel planning is carried out to reduce the risk for injury to vital blood vessels and nerves. In the navigation system, the robot can obtain visual perception ability from a visual receptor and automatically adjust the robotic platform and robot arm to the appropriate positions according to the patient's position and preoperative plan. In addition, the robot can automatically register the surgical levels through intraoperative fluoroscopy. After that, the robot will provide navigation using the 6 degree-of-freedom (DOF) robot arm according to the preoperative planning system and guide the surgeon to complete the establishment of the working channel. In the foraminoplasty system, according to the foraminoplasty planning in the preoperative planning system, the robot performs foraminoplasty automatically using the high speed burr at the end of the robot arm. The system can provide real-time feedback on the working status of the bur through multi-mode sensors such as multidimensional force, position, and acceleration. Finally, a prototype of the system is constructed and performance tests are conducted.
DISCUSSION CONCLUSIONS
Our study will develop a robot-assisted system to perform transforaminal PELS, and this robot-assisted system can also be used for other percutaneous endoscopic spinal surgeries such as interlaminar PELS and percutaneous endoscopic cervical and thoracic surgeries through further research. The development of this robot-assisted system can be of great significance. First, the robot can improve the accuracy and efficiency of endoscopic spinal surgeries. In addition, it can avoid multiple intraoperative fluoroscopies, minimize exposure to both patients and the surgical staff, shorten the operative time, and improve the learning curve of beginners, which is beneficial to the popularization of percutaneous endoscopic spinal surgeries.

Identifiants

pubmed: 33076965
doi: 10.1186/s13018-020-02003-y
pii: 10.1186/s13018-020-02003-y
pmc: PMC7569762
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

479

Subventions

Organisme : Beijing Municipal Science &Technology Commission
ID : No. Z191100007619036

Références

Int Orthop. 2019 Apr;43(4):923-937
pubmed: 30547214
Spine (Phila Pa 1976). 2010 May 15;35(11):E465-70
pubmed: 20473117
Pain Physician. 2019 May;22(3):295-304
pubmed: 31151337
Sci Rep. 2020 Jan 28;10(1):1305
pubmed: 31992790
Neurosurg Clin N Am. 2020 Jan;31(1):103-110
pubmed: 31739920
Biomed Res Int. 2017;2017:3610385
pubmed: 29226132
Spine (Phila Pa 1976). 2018 Dec 1;43(23):1670-1677
pubmed: 29672420
Neurosurg Clin N Am. 2020 Jan;31(1):25-32
pubmed: 31739926
Neurosurg Clin N Am. 2020 Jan;31(1):1-7
pubmed: 31739919
Sci Rep. 2020 May 5;10(1):7522
pubmed: 32371880
J Orthop Sci. 2018 Mar;23(2):229-236
pubmed: 29248305
Expert Rev Med Devices. 2012 Jul;9(4):361-6
pubmed: 22905840
Ann Transl Med. 2018 Mar;6(6):100
pubmed: 29707549
Eur Spine J. 2018 Apr;27(4):921-930
pubmed: 29032475
Int J Surg. 2016 Jul;31:86-92
pubmed: 27260312
J Neurol Surg A Cent Eur Neurosurg. 2013 Jul;74(4):258-61
pubmed: 23315671
J Robot Surg. 2013 Jun;7(2):177-85
pubmed: 27000910
Pain Physician. 2020 Jan;23(1):49-56
pubmed: 32013278
J Korean Neurosurg Soc. 2017 Sep;60(5):485-497
pubmed: 28881110
Int Orthop. 2019 Apr;43(4):909-916
pubmed: 30612170
Spine (Phila Pa 1976). 2011 Jan 15;36(2):E139-43
pubmed: 20948463
Neurosurg Clin N Am. 1996 Jan;7(1):59-63
pubmed: 8835146
Spine J. 2020 Apr;20(4):629-637
pubmed: 31863933
Int J Comput Assist Radiol Surg. 2019 Dec;14(12):2123-2135
pubmed: 31317475
Ann Transl Med. 2019 May;7(10):224
pubmed: 31297389
Spine (Phila Pa 1976). 2019 Aug 1;44(15):1097-1104
pubmed: 30830046
Spine (Phila Pa 1976). 2020 Jan 15;45(2):E111-E119
pubmed: 31404053
World Neurosurg. 2019 Dec;132:47-52
pubmed: 31442650
Spine (Phila Pa 1976). 2009 Sep 1;34(19):2104-9
pubmed: 19730218
World Neurosurg. 2019 Aug;128:e504-e512
pubmed: 31051300
Int Orthop. 2020 Feb;44(2):309-317
pubmed: 31773186
J Spinal Disord Tech. 2011 Oct;24(7):421-31
pubmed: 21430567
Eur Spine J. 2018 Jul;27(Suppl 3):465-471
pubmed: 29353327

Auteurs

Ning Fan (N)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.

Shuo Yuan (S)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.

Peng Du (P)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.

Wenyi Zhu (W)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.

Liang Li (L)

Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

Yong Hai (Y)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.

Hui Ding (H)

Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

Guangzhi Wang (G)

Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China. wgz-dea@tsinghua.edu.cn.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. wgz-dea@tsinghua.edu.cn.

Lei Zang (L)

Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. zanglei@ccmu.edu.cn.
Chaoyang-Tsinghua Digitization & Artificial Intelligence Orthopedic Laboratory, Beijing, China. zanglei@ccmu.edu.cn.

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