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

Abstract

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 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 target 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 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.

A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery

“…In the previous review of the literature, the combination of arti cial intelligence and endoscopic surgery manifested itself mainly in the placement of computer-navigated endoscopic working channels [20,21] .In contrast, research reports on spinal endoscopic surgical images are blank.Peng Cui [22] et al developed a CAD system based on YOLOv3 that is a deep learning algorithm architecture to identify nerve roots and dural sac in spinal endoscopic surgical images,which the sensitivity, speci city and accuracy can reach 90.90%, 93.68%, and 92.29% respectively. However, the algorithm they developed identi ed few structures, and the selected pictures failed to fully cover the entire endoscopic surgical procedure, such as ligamentum avum exposure, radiofrequency electrode hemostasis, bite removal of lamina.…”