Development and clinical application of robot-assisted technology in traumatic orthopedics_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

ZHU Zhenzhong ¹ , ZHENG Guoyan ² ,  ZHANG Changqing ¹

1. Department of Orthopedic Surgery, Shanghai Jiao Tong University Affiliated Shanghai Sixth People’s Hospital, Shanghai, 200233, P. R. China;
2. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China;

Corresponding author：

ZHANG Changqing, Email: Zhangcq@sjtu.edu.cn

Keywords：

Robot-assisted technology; traumatic orthopedics; fracture reduction; intraoperative navigation

DOI：

10.7507/1002-1892.202206097

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To review and evaluate the basic principles and advantages of orthopedic robot-assisted technology, research progress, clinical applications, and limitations in the field of traumatic orthopedics, especially in fracture reduction robots. Methods An extensive review of research literature on the principles of robot-assisted technology and fracture reduction robots was conducted to analyze the technical advantages and clinical efficacy and shortcomings, and to discuss the future development trends in this field. Results Orthopedic surgical robots can assist orthopedists in intuitive preoperative planning, precise intraoperative control, and minimally invasive operations. It greatly expands the ability of doctors to evaluate and treat orthopedic trauma. Trauma orthopedic surgery robot has achieved a breakthrough from basic research to clinical application, and the preliminary results show that the technology can significantly improve surgical precision and reduce surgical trauma. However, there are still problems such as insufficient evaluation of effectiveness, limited means of technology realization, and narrow clinical indications that need to be solved. Conclusion Robot-assisted technology has a broad application prospect in traumatic orthopedics, but the current development is still in the initial stage. It is necessary to strengthen the cooperative medical-industrial research, the construction of doctors’ communication platform, standardized training and data sharing in order to continuously promote the development of robot-assisted technology in traumatic orthopedics and better play its clinical application value.

Citation： ZHU Zhenzhong, ZHENG Guoyan, ZHANG Changqing. Development and clinical application of robot-assisted technology in traumatic orthopedics. Chinese Journal of Reparative and Reconstructive Surgery, 2022, 36(8): 915-922. doi: 10.7507/1002-1892.202206097 Copy

1.	Kwoh YS, Hou J, Jonckheere EA, et al. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng, 1988, 35(2): 153-160.
2.	Goldsmith MF. For better hip replacement results, surgeon’s best friend may be a robot. JAMA, 1992, 267(5): 613-614.
3.	Nolte LP, Zamorano LJ, Jiang Z, et al. Image-guided insertion of transpedicular screws. A laboratory set-up. Spine (Phila Pa 1976), 1995, 20(4): 497-500.
4.	Domb BG, El Bitar YF, Sadik AY, et al. Comparison of robotic-assisted and conventional acetabular cup placement in THA: a matched-pair controlled study. Clin Orthop Relat Res, 2014, 472(1): 329-336.
5.	Redmond JM, Gupta A, Hammarstedt JE, et al. Accuracy of component placement in robotic-assisted total hip arthroplasty. Orthopedics, 2016, 39(3): 193-199.
6.	Mezger U, Jendrewski C, Bartels M. Navigation in surgery. Langenbecks Arch Surg, 2013, 398(4): 501-514.
7.	Pflugi S, Liu L, Ecker TM, et al. A cost-effective surgical navigation solution for periacetabular osteotomy (PAO) surgery. Int J Comput Assist Radiol Surg, 2016, 11(2): 271-280.
8.	Hofstetter R, Slomczykowski M, Sati M, et al. Fluoroscopy as an imaging means for computer-assisted surgical navigation. Comput Aided Surg, 1999, 4(2): 65-76.
9.	Malham GM, Munday NR. Comparison of novel machine vision spinal image guidance system with existing 3D fluoroscopy-based navigation system: a randomized prospective study. Spine J, 2022, 22(4): 561-569.
10.	Bargar WL, Parise CA, Hankins A, et al. Fourteen year follow-up of randomized clinical trials of active robotic-assisted total hip arthroplasty. J Arthroplasty, 2018, 33(3): 810-814.
11.	Adams SB, Spritzer CE, Hofstaetter SG, et al. Computer-assisted tibia preparation for total ankle arthroplasty: a cadaveric study. Int J Med Robot, 2007, 3(4): 336-340.
12.	Reb CW, Berlet GC. Experience with navigation in total ankle arthroplasty. Is it worth the cost? Foot Ankle Clin, 2017, 22(2): 455-463.
13.	Wiewiorski M, Valderrabano V, Kretzschmar M, et al. CT-guided robotically-assisted infiltration of foot and ankle joints. Minim Invasive Ther Allied Technol, 2009, 18(5): 291-296.
14.	Bozkurt M, Apaydin N, Işik C, et al. Robotic arthroscopic surgery: a new challenge in arthroscopic surgery Part-Ⅰ: Robotic shoulder arthroscopy; a cadaveric feasibility study. Int J Med Robot, 2011, 7(4): 496-500.
15.	Bargar WL. Robots in orthopaedic surgery: past, present, and future. Clin Orthop Relat Res, 2007, 463: 31-36.
16.	Lang JE, Mannava S, Floyd AJ, et al. Robotic systems in orthopaedic surgery. J Bone Joint Surg (Br), 2011, 93(10): 1296-1299.
17.	Boylan M, Suchman K, Vigdorchik J, et al. Technology-assisted hip and knee arthroplasties: An analysis of utilization trends. J Arthroplasty, 2018, 33(4): 1019-1023.
18.	Morelli L, Guadagni S, Di Franco G, et al. Da Vinci single site© surgical platform in clinical practice: a systematic review. Int J Med Robot, 2016, 12(4): 724-734.
19.	Chun YS, Kim KI, Cho YJ, et al. Causes and patterns of aborting a robot-assisted arthroplasty. J Arthroplasty, 2011, 26(4): 621-625.
20.	Du Z, Wang W, Yan Z, et al. Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator. Sensors (Basel), 2017, 17(4): 844. doi: 10.3390/s17040844.
21.	Winemaker MJ. Perfect balance in total knee arthroplasty: the elusive compromise. J Arthroplasty, 2002, 17(1): 2-10.
22.	Kayani B, Konan S, Pietrzak JRT, et al. Iatrogenic bone and soft tissue trauma in robotic-arm assisted total knee arthroplasty compared with conventional jig-based total knee arthroplasty: A prospective cohort study and validation of a new classification system. J Arthroplasty, 2018, 33(8): 2496-2501.
23.	Chang JS, Kayani B, Wallace C, et al. Functional alignment achieves soft-tissue balance in total knee arthroplasty as measured with quantitative sensor-guided technology. Bone Joint J, 2021, 103-B(3): 507-514.
24.	Tian W, Fan M, Zeng C, et al. Telerobotic spinal surgery based on 5G network: The first 12 cases. Neurospine, 2020, 17(1): 114-120.
25.	Westphal R, Winkelbach S, Wahl F, et al. Robot-assisted long bone fracture reduction. Int J Robot Res, 2009, 28(10): 1259-1278.
26.	Kim YH, Lee SG. Computer and robotic model of external fixation system for fracture treatment. Computational Science-Iccs 2004 Proceedings, 2004, 3039: 1081-1087.
27.	Koo TK, Chao EY, Mak AF. Development and validation of a new approach for computer-aided long bone fracture reduction using unilateral external fixator. J Biomech, 2006, 39(11): 2104-2112.
28.	Viceconti M, Sudanese A, Toni A, et al. A software simulation of tibial fracture reduction with external fixator. Comput Methods Programs Biomed, 1993, 40(2): 89-94.
29.	Viceconti M, Andrisano AO, Toni A, et al. Automatic fracture reduction with a computer-controlled external fixator. Med Eng Phys, 1994, 16(2): 143-149.
30.	Seide K, Wolter D. Corrections using the hexapod. Orthopade, 2000, 29(1): 39-46.
31.	Seide K, Wolter D, Kortmann HR. Fracture reduction and deformity correction with the hexapod Ilizarov fixator. Clin Orthop Relat Res, 1999, (363): 186-195.
32.	Seide K, Weinrich N, Wenzl ME, et al. Three-dimensional load measurements in an external fixator. J Biomech, 2004, 37(9): 1361-1369.
33.	Faschingbauer M, Heuer HJ, Seide K, et al. Accuracy of a hexapod parallel robot kinematics based external fixator. Int J Med Robot, 2015, 11(4): 424-435.
34.	Tang PF, Hu L, Du HL, et al. Novel 3D hexapod computer-assisted orthopaedic surgery system for closed diaphyseal fracture reduction. Int J Med Robot, 2012, 8(1): 17-24.
35.	Füchtmeier B, Egersdoerfer S, Mai R, et al. Reduction of femoral shaft fractures in vitro by a new developed reduction robot system ‘RepoRobo’. Injury, 2004, 35 Suppl 1: S-A113S-119S. doi: 10.1016/j.injury.2004.05.019.
36.	Westphal R, Winkelbach S, Gösling T, et al. A surgical telemanipulator for femur shaft fracture reduction. Int J Med Robot, 2006, 2(3): 238-250.
37.	Ye R, Chen Y, Ieee. Development of a six degree of freedom (DOF) hybrid robot for femur shaft fracture reduction. IEEE International Conference on Robotics and Biomimetics (ROBIO), Bangkok, 2009: 306-311.
38.	Dagnino G, Georgilas I, Tarassoli P, et al. Vision-based real-time position control of a semi-automated system for robot-assisted joint fracture surgery. Int J Comput Assist Radiol Surg, 2016, 11(3): 437-455.
39.	Wang T, Li C, Hu L, et al. A removable hybrid robot system for long bone fracture reduction. Biomed Mater Eng, 2014, 24(1): 501-509.
40.	Maeda Y, Sugano N, Saito M, et al. Robot-assisted femoral fracture reduction: preliminary study in patients and healthy volunteers. Comput Aided Surg, 2008, 13(3): 148-156.
41.	Joung S, Kamon H, Liao H, et al. A robot assisted hip fracture reduction with a navigation system. Med Image Comput Comput Assist Interv, 2008, 11(Pt 2): 501-508.
42.	Joung S, Liao H, Kobayashi E, et al. Hazard analysis of fracture-reduction robot and its application to safety design of fracture-reduction assisting robotic system. 2010 IEEE International Conference on Robotics and Automation, 2010: 1554-1561.
43.	Hung SS, Lee MY. Functional assessment of a surgical robot for reduction of lower limb fractures. Int J Med Robot, 2010, 6(4): 413-421.
44.	Sun XG, Zhu Q, Wang XS, et al. A remote control robotic surgical system for femur shaft fracture reduction. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015: 1649-1653.
45.	Du D, Liu Z, Omori S, et al. Computer-aided parachute guiding system for closed reduction of diaphyseal fractures. Int J Med Robot, 2014, 10(3): 325-331.
46.	Bouazza-Marouf K, Browbank I, Hewit JR. Robotic-assisted internal fixation of femoral fractures. Proc Inst Mech Eng H, 1995, 209(1): 51-58.
47.	苏永刚, 孙志彬, 朱罡, 等. 基于体感交互的骨折复位机器人控制方法实验研究. 中国生物医学工程学报, 2016, 35(3): 380-384.
48.	Li C, Wang T, Hu L, et al. A visual servo-based teleoperation robot system for closed diaphyseal fracture reduction. Proc Inst Mech Eng H, 2015, 229(9): 629-637.
49.	Cutolo F, Carli S, Parchi PD, et al. AR interaction paradigm for closed reduction of long-bone fractures via external fixation. 22nd ACM Conference on Virtual Reality Software and Technology (VRST) Munich, Germany, 2016: 305-306.
50.	Wang JQ, Wang Y, Feng Y, et al. Percutaneous sacroiliac screw placement: A prospective randomized comparison of robot-assisted navigation procedures with a conventional technique. Chin Med J (Engl), 2017, 130(21): 2527-2534.
51.	Liu HS, Duan SJ, Liu SD, et al. Robot-assisted percutaneous screw placement combined with pelvic internal fixator for minimally invasive treatment of unstable pelvic ring fractures. Int J Med Robot, 2018, 14(5): e1927. doi: 10.1002/rcs.1927.
52.	Liu HS, Duan SJ, Xin FZ, et al. Robot-assisted minimally-invasive internal fixation of pelvic ring injuries: A single-center experience. Orthop Surg, 2019, 11(1): 42-51.
53.	Long T, Li KN, Gao JH, et al. Comparative study of percutaneous sacroiliac screw with or without tirobot assistance for treating pelvic posterior ring fractures. Orthop Surg, 2019, 11(3): 386-396.
54.	Suero EM, Westphal R, Citak M, et al. Robotic technique improves entry point alignment for intramedullary nailing of femur fractures compared to the conventional technique: a cadaveric study. J Robot Surg, 2018, 12(2): 311-315.
55.	Panzica M, Suero EM, Westphal R, et al. Robotic distal locking of intramedullary nailing: Technical description and cadaveric testing. Int J Med Robot, 2017, 13(4). doi: 10.1002/rcs.1831.
56.	Lan H, Tan Z, Li KN, et al. Intramedullary nail fixation assisted by orthopaedic robot navigation for intertrochanteric fractures in elderly patients. Orthop Surg, 2019, 11(2): 255-262.
57.	He M, Han W, Zhao CP, et al. Evaluation of a Bi-planar robot navigation system for insertion of cannulated screws in femoral neck fractures. Orthop Surg, 2019, 11(3): 373-379.
58.	Duan SJ, Liu HS, Wu WC, et al. Robot-assisted percutaneous cannulated screw fixation of femoral neck fractures: Preliminary clinical results. Orthop Surg, 2019, 11(1): 34-41.
59.	Liu B, Wu F, Chen S, et al. Robot-assisted percutaneous scaphoid fracture fixation: a report of ten patients. J Hand Surg (Eur Vol), 2019, 44(7): 685-691.
60.	Leow A, Huang SC, Geng A, et al. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. Inf Process Med Imaging, 2005, 19: 493-503.
61.	Liu R, Li Z, Fan X, et al. Learning deformable image registration from optimization: Perspective, modules, bilevel training and beyond. IEEE Trans Pattern Anal Mach Intell, 2021. doi: 10.1109/TPAMI.2021.3115825.
62.	Lei Y, Fu Y, Wang T, et al. 4D-CT deformable image registration using multiscale unsupervised deep learning. Phys Med Biol, 2020, 65(8): 085003. doi: 10.1088/1361-6560/ab79c4.

1. Kwoh YS, Hou J, Jonckheere EA, et al. A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng, 1988, 35(2): 153-160.
2. Goldsmith MF. For better hip replacement results, surgeon’s best friend may be a robot. JAMA, 1992, 267(5): 613-614.
3. Nolte LP, Zamorano LJ, Jiang Z, et al. Image-guided insertion of transpedicular screws. A laboratory set-up. Spine (Phila Pa 1976), 1995, 20(4): 497-500.
4. Domb BG, El Bitar YF, Sadik AY, et al. Comparison of robotic-assisted and conventional acetabular cup placement in THA: a matched-pair controlled study. Clin Orthop Relat Res, 2014, 472(1): 329-336.
5. Redmond JM, Gupta A, Hammarstedt JE, et al. Accuracy of component placement in robotic-assisted total hip arthroplasty. Orthopedics, 2016, 39(3): 193-199.
6. Mezger U, Jendrewski C, Bartels M. Navigation in surgery. Langenbecks Arch Surg, 2013, 398(4): 501-514.
7. Pflugi S, Liu L, Ecker TM, et al. A cost-effective surgical navigation solution for periacetabular osteotomy (PAO) surgery. Int J Comput Assist Radiol Surg, 2016, 11(2): 271-280.
8. Hofstetter R, Slomczykowski M, Sati M, et al. Fluoroscopy as an imaging means for computer-assisted surgical navigation. Comput Aided Surg, 1999, 4(2): 65-76.
9. Malham GM, Munday NR. Comparison of novel machine vision spinal image guidance system with existing 3D fluoroscopy-based navigation system: a randomized prospective study. Spine J, 2022, 22(4): 561-569.
10. Bargar WL, Parise CA, Hankins A, et al. Fourteen year follow-up of randomized clinical trials of active robotic-assisted total hip arthroplasty. J Arthroplasty, 2018, 33(3): 810-814.
11. Adams SB, Spritzer CE, Hofstaetter SG, et al. Computer-assisted tibia preparation for total ankle arthroplasty: a cadaveric study. Int J Med Robot, 2007, 3(4): 336-340.
12. Reb CW, Berlet GC. Experience with navigation in total ankle arthroplasty. Is it worth the cost? Foot Ankle Clin, 2017, 22(2): 455-463.
13. Wiewiorski M, Valderrabano V, Kretzschmar M, et al. CT-guided robotically-assisted infiltration of foot and ankle joints. Minim Invasive Ther Allied Technol, 2009, 18(5): 291-296.
14. Bozkurt M, Apaydin N, Işik C, et al. Robotic arthroscopic surgery: a new challenge in arthroscopic surgery Part-Ⅰ: Robotic shoulder arthroscopy; a cadaveric feasibility study. Int J Med Robot, 2011, 7(4): 496-500.
15. Bargar WL. Robots in orthopaedic surgery: past, present, and future. Clin Orthop Relat Res, 2007, 463: 31-36.
16. Lang JE, Mannava S, Floyd AJ, et al. Robotic systems in orthopaedic surgery. J Bone Joint Surg (Br), 2011, 93(10): 1296-1299.
17. Boylan M, Suchman K, Vigdorchik J, et al. Technology-assisted hip and knee arthroplasties: An analysis of utilization trends. J Arthroplasty, 2018, 33(4): 1019-1023.
18. Morelli L, Guadagni S, Di Franco G, et al. Da Vinci single site© surgical platform in clinical practice: a systematic review. Int J Med Robot, 2016, 12(4): 724-734.
19. Chun YS, Kim KI, Cho YJ, et al. Causes and patterns of aborting a robot-assisted arthroplasty. J Arthroplasty, 2011, 26(4): 621-625.
20. Du Z, Wang W, Yan Z, et al. Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator. Sensors (Basel), 2017, 17(4): 844. doi: 10.3390/s17040844.
21. Winemaker MJ. Perfect balance in total knee arthroplasty: the elusive compromise. J Arthroplasty, 2002, 17(1): 2-10.
22. Kayani B, Konan S, Pietrzak JRT, et al. Iatrogenic bone and soft tissue trauma in robotic-arm assisted total knee arthroplasty compared with conventional jig-based total knee arthroplasty: A prospective cohort study and validation of a new classification system. J Arthroplasty, 2018, 33(8): 2496-2501.
23. Chang JS, Kayani B, Wallace C, et al. Functional alignment achieves soft-tissue balance in total knee arthroplasty as measured with quantitative sensor-guided technology. Bone Joint J, 2021, 103-B(3): 507-514.
24. Tian W, Fan M, Zeng C, et al. Telerobotic spinal surgery based on 5G network: The first 12 cases. Neurospine, 2020, 17(1): 114-120.
25. Westphal R, Winkelbach S, Wahl F, et al. Robot-assisted long bone fracture reduction. Int J Robot Res, 2009, 28(10): 1259-1278.
26. Kim YH, Lee SG. Computer and robotic model of external fixation system for fracture treatment. Computational Science-Iccs 2004 Proceedings, 2004, 3039: 1081-1087.
27. Koo TK, Chao EY, Mak AF. Development and validation of a new approach for computer-aided long bone fracture reduction using unilateral external fixator. J Biomech, 2006, 39(11): 2104-2112.
28. Viceconti M, Sudanese A, Toni A, et al. A software simulation of tibial fracture reduction with external fixator. Comput Methods Programs Biomed, 1993, 40(2): 89-94.
29. Viceconti M, Andrisano AO, Toni A, et al. Automatic fracture reduction with a computer-controlled external fixator. Med Eng Phys, 1994, 16(2): 143-149.
30. Seide K, Wolter D. Corrections using the hexapod. Orthopade, 2000, 29(1): 39-46.
31. Seide K, Wolter D, Kortmann HR. Fracture reduction and deformity correction with the hexapod Ilizarov fixator. Clin Orthop Relat Res, 1999, (363): 186-195.
32. Seide K, Weinrich N, Wenzl ME, et al. Three-dimensional load measurements in an external fixator. J Biomech, 2004, 37(9): 1361-1369.
33. Faschingbauer M, Heuer HJ, Seide K, et al. Accuracy of a hexapod parallel robot kinematics based external fixator. Int J Med Robot, 2015, 11(4): 424-435.
34. Tang PF, Hu L, Du HL, et al. Novel 3D hexapod computer-assisted orthopaedic surgery system for closed diaphyseal fracture reduction. Int J Med Robot, 2012, 8(1): 17-24.
35. Füchtmeier B, Egersdoerfer S, Mai R, et al. Reduction of femoral shaft fractures in vitro by a new developed reduction robot system ‘RepoRobo’. Injury, 2004, 35 Suppl 1: S-A113S-119S. doi: 10.1016/j.injury.2004.05.019.
36. Westphal R, Winkelbach S, Gösling T, et al. A surgical telemanipulator for femur shaft fracture reduction. Int J Med Robot, 2006, 2(3): 238-250.
37. Ye R, Chen Y, Ieee. Development of a six degree of freedom (DOF) hybrid robot for femur shaft fracture reduction. IEEE International Conference on Robotics and Biomimetics (ROBIO), Bangkok, 2009: 306-311.
38. Dagnino G, Georgilas I, Tarassoli P, et al. Vision-based real-time position control of a semi-automated system for robot-assisted joint fracture surgery. Int J Comput Assist Radiol Surg, 2016, 11(3): 437-455.
39. Wang T, Li C, Hu L, et al. A removable hybrid robot system for long bone fracture reduction. Biomed Mater Eng, 2014, 24(1): 501-509.
40. Maeda Y, Sugano N, Saito M, et al. Robot-assisted femoral fracture reduction: preliminary study in patients and healthy volunteers. Comput Aided Surg, 2008, 13(3): 148-156.
41. Joung S, Kamon H, Liao H, et al. A robot assisted hip fracture reduction with a navigation system. Med Image Comput Comput Assist Interv, 2008, 11(Pt 2): 501-508.
42. Joung S, Liao H, Kobayashi E, et al. Hazard analysis of fracture-reduction robot and its application to safety design of fracture-reduction assisting robotic system. 2010 IEEE International Conference on Robotics and Automation, 2010: 1554-1561.
43. Hung SS, Lee MY. Functional assessment of a surgical robot for reduction of lower limb fractures. Int J Med Robot, 2010, 6(4): 413-421.
44. Sun XG, Zhu Q, Wang XS, et al. A remote control robotic surgical system for femur shaft fracture reduction. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015: 1649-1653.
45. Du D, Liu Z, Omori S, et al. Computer-aided parachute guiding system for closed reduction of diaphyseal fractures. Int J Med Robot, 2014, 10(3): 325-331.
46. Bouazza-Marouf K, Browbank I, Hewit JR. Robotic-assisted internal fixation of femoral fractures. Proc Inst Mech Eng H, 1995, 209(1): 51-58.
47. 苏永刚, 孙志彬, 朱罡, 等. 基于体感交互的骨折复位机器人控制方法实验研究. 中国生物医学工程学报, 2016, 35(3): 380-384.
48. Li C, Wang T, Hu L, et al. A visual servo-based teleoperation robot system for closed diaphyseal fracture reduction. Proc Inst Mech Eng H, 2015, 229(9): 629-637.
49. Cutolo F, Carli S, Parchi PD, et al. AR interaction paradigm for closed reduction of long-bone fractures via external fixation. 22nd ACM Conference on Virtual Reality Software and Technology (VRST) Munich, Germany, 2016: 305-306.
50. Wang JQ, Wang Y, Feng Y, et al. Percutaneous sacroiliac screw placement: A prospective randomized comparison of robot-assisted navigation procedures with a conventional technique. Chin Med J (Engl), 2017, 130(21): 2527-2534.
51. Liu HS, Duan SJ, Liu SD, et al. Robot-assisted percutaneous screw placement combined with pelvic internal fixator for minimally invasive treatment of unstable pelvic ring fractures. Int J Med Robot, 2018, 14(5): e1927. doi: 10.1002/rcs.1927.
52. Liu HS, Duan SJ, Xin FZ, et al. Robot-assisted minimally-invasive internal fixation of pelvic ring injuries: A single-center experience. Orthop Surg, 2019, 11(1): 42-51.
53. Long T, Li KN, Gao JH, et al. Comparative study of percutaneous sacroiliac screw with or without tirobot assistance for treating pelvic posterior ring fractures. Orthop Surg, 2019, 11(3): 386-396.
54. Suero EM, Westphal R, Citak M, et al. Robotic technique improves entry point alignment for intramedullary nailing of femur fractures compared to the conventional technique: a cadaveric study. J Robot Surg, 2018, 12(2): 311-315.
55. Panzica M, Suero EM, Westphal R, et al. Robotic distal locking of intramedullary nailing: Technical description and cadaveric testing. Int J Med Robot, 2017, 13(4). doi: 10.1002/rcs.1831.
56. Lan H, Tan Z, Li KN, et al. Intramedullary nail fixation assisted by orthopaedic robot navigation for intertrochanteric fractures in elderly patients. Orthop Surg, 2019, 11(2): 255-262.
57. He M, Han W, Zhao CP, et al. Evaluation of a Bi-planar robot navigation system for insertion of cannulated screws in femoral neck fractures. Orthop Surg, 2019, 11(3): 373-379.
58. Duan SJ, Liu HS, Wu WC, et al. Robot-assisted percutaneous cannulated screw fixation of femoral neck fractures: Preliminary clinical results. Orthop Surg, 2019, 11(1): 34-41.
59. Liu B, Wu F, Chen S, et al. Robot-assisted percutaneous scaphoid fracture fixation: a report of ten patients. J Hand Surg (Eur Vol), 2019, 44(7): 685-691.
60. Leow A, Huang SC, Geng A, et al. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. Inf Process Med Imaging, 2005, 19: 493-503.
61. Liu R, Li Z, Fan X, et al. Learning deformable image registration from optimization: Perspective, modules, bilevel training and beyond. IEEE Trans Pattern Anal Mach Intell, 2021. doi: 10.1109/TPAMI.2021.3115825.
62. Lei Y, Fu Y, Wang T, et al. 4D-CT deformable image registration using multiscale unsupervised deep learning. Phys Med Biol, 2020, 65(8): 085003. doi: 10.1088/1361-6560/ab79c4.

Chinese Journal of Reparative and Reconstructive Surgery

Development and clinical application of robot-assisted technology in traumatic orthopedics

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