Design of intelligent human-machine collaborative robot-assisted craniotomy system
Objectives: To develop an intelligent human-machine collaborative control robot-assisted craniotomy system, and test its efficacy by experiments. Methods: The system integrated a UR5 robotic arm (Universal Robots, Denmark), a host computer, a double six-degree-of-freedom force sensor(Nanjing Yuli In...
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Elsevier
2024-12-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024163955 |
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| author | Meng Cui Wenqing Ren Tengfei Cui Ruifeng Chen Yi Shan Xiaodong Ma |
| author_facet | Meng Cui Wenqing Ren Tengfei Cui Ruifeng Chen Yi Shan Xiaodong Ma |
| author_sort | Meng Cui |
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| description | Objectives: To develop an intelligent human-machine collaborative control robot-assisted craniotomy system, and test its efficacy by experiments. Methods: The system integrated a UR5 robotic arm (Universal Robots, Denmark), a host computer, a double six-degree-of-freedom force sensor(Nanjing Yuli Instrument Co., Ltd.), a medical drill(AESCULAP®, Germany), a Polaris Optical navigation system(NDI,Canada), with a self-designed navigation procedure and a visual graphical user interface(GUI). According to a preoperative CT and resection plan, the motion of robotic arm can be restricted in a precise and safe area. Through experiments of the 3D-printed skull models and animals (Bama mini pig), we tested the accuracy, efficiency and safety of the robot system. Results: After successfully developed the robot-assisted craniotomy system, we tested the collaborative controlling fluency of robotic arm with the average response time less than 1 s, as well as feedback sensitivity of force sensor with an average result of 60 N and 50 N when drilling on skull models and mini pigs respectively. In addition, compared with “surgeon” group, “robot” group had less average positioning error (1.87 ± 0.66 mm VS 3.14 ± 0.73 mm, P < 0.001) and time spent (6.64 ± 1.15min VS 8.06 ± 1.10min, P = 0.001) in skull model experiments. Also, in mini pig experiments, “robot” group had less average positioning error (3.26 ± 0.51 mm VS 4.39 ± 0.75 mm, P = 0.008) and time spent (11.83 ± 0.92min VS 26.10 ± 1.62min, P < 0.001) compared with “surgeon” group. No matter in skull model experiments or in mini pig experiments, the durations of robot startup and navigation process were not different between the experimental group and control group (3.44 ± 0.98 VS 3.75 ± 1.00min, P = 0.39 [skull model experiments]; 6.42 ± 0.65 VS 7.10 ± 1.12min, P = 0.11 [mini pig experiments]). Because of limited samples, we compared the incidence of tissue injury between “robot” and “surgeon” group jointly (3.8 % [1/26] VS 19.2 % [5/26], P = 0.193). Conclusion: Successfully developed, the human-machine collaborative robot-assisted craniotomy system achieved craniotomy procedure fluently providing a sensitive force feedback to surgeon and did better than manual work by surgeon in accuracy, efficiency and safety. Further experimental research needs to be performed to testify its applicability in neurosurgery in future. |
| format | Article |
| id | doaj-art-61799d5d24e145bcaaa5552c7d5ebddc |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| spelling | doaj-art-61799d5d24e145bcaaa5552c7d5ebddc2024-12-13T10:58:42ZengElsevierHeliyon2405-84402024-12-011023e40364Design of intelligent human-machine collaborative robot-assisted craniotomy systemMeng Cui0Wenqing Ren1Tengfei Cui2Ruifeng Chen3Yi Shan4Xiaodong Ma5Department of Emergency Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China; Corresponding author.Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, ChinaInstitute of Intelligent Robotics, Beijing Institute of Technology, Beijing, 100081, ChinaDepartment of Emergency Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China; Corresponding author.Department of Emergency Medicine, The Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China; Corresponding author.Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China; Corresponding author.Objectives: To develop an intelligent human-machine collaborative control robot-assisted craniotomy system, and test its efficacy by experiments. Methods: The system integrated a UR5 robotic arm (Universal Robots, Denmark), a host computer, a double six-degree-of-freedom force sensor(Nanjing Yuli Instrument Co., Ltd.), a medical drill(AESCULAP®, Germany), a Polaris Optical navigation system(NDI,Canada), with a self-designed navigation procedure and a visual graphical user interface(GUI). According to a preoperative CT and resection plan, the motion of robotic arm can be restricted in a precise and safe area. Through experiments of the 3D-printed skull models and animals (Bama mini pig), we tested the accuracy, efficiency and safety of the robot system. Results: After successfully developed the robot-assisted craniotomy system, we tested the collaborative controlling fluency of robotic arm with the average response time less than 1 s, as well as feedback sensitivity of force sensor with an average result of 60 N and 50 N when drilling on skull models and mini pigs respectively. In addition, compared with “surgeon” group, “robot” group had less average positioning error (1.87 ± 0.66 mm VS 3.14 ± 0.73 mm, P < 0.001) and time spent (6.64 ± 1.15min VS 8.06 ± 1.10min, P = 0.001) in skull model experiments. Also, in mini pig experiments, “robot” group had less average positioning error (3.26 ± 0.51 mm VS 4.39 ± 0.75 mm, P = 0.008) and time spent (11.83 ± 0.92min VS 26.10 ± 1.62min, P < 0.001) compared with “surgeon” group. No matter in skull model experiments or in mini pig experiments, the durations of robot startup and navigation process were not different between the experimental group and control group (3.44 ± 0.98 VS 3.75 ± 1.00min, P = 0.39 [skull model experiments]; 6.42 ± 0.65 VS 7.10 ± 1.12min, P = 0.11 [mini pig experiments]). Because of limited samples, we compared the incidence of tissue injury between “robot” and “surgeon” group jointly (3.8 % [1/26] VS 19.2 % [5/26], P = 0.193). Conclusion: Successfully developed, the human-machine collaborative robot-assisted craniotomy system achieved craniotomy procedure fluently providing a sensitive force feedback to surgeon and did better than manual work by surgeon in accuracy, efficiency and safety. Further experimental research needs to be performed to testify its applicability in neurosurgery in future.http://www.sciencedirect.com/science/article/pii/S2405844024163955Medical roboticsCraniotomyHuman-machine collaborationImage-guided surgery |
| spellingShingle | Meng Cui Wenqing Ren Tengfei Cui Ruifeng Chen Yi Shan Xiaodong Ma Design of intelligent human-machine collaborative robot-assisted craniotomy system Heliyon Medical robotics Craniotomy Human-machine collaboration Image-guided surgery |
| title | Design of intelligent human-machine collaborative robot-assisted craniotomy system |
| title_full | Design of intelligent human-machine collaborative robot-assisted craniotomy system |
| title_fullStr | Design of intelligent human-machine collaborative robot-assisted craniotomy system |
| title_full_unstemmed | Design of intelligent human-machine collaborative robot-assisted craniotomy system |
| title_short | Design of intelligent human-machine collaborative robot-assisted craniotomy system |
| title_sort | design of intelligent human machine collaborative robot assisted craniotomy system |
| topic | Medical robotics Craniotomy Human-machine collaboration Image-guided surgery |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024163955 |
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