CT-to-fluoroscopy registration versus scan-and-plan registration for robot-assisted insertion of lumbar pedicle screws

Flattening the learning curve - Early experience of robotic-assisted pedicle screw placement in spine surgery

Aims and objectives

To determine accuracy of pedicle screws placed by freehand, fluoroscopy-assistance and robotic-assistance with intraoperative image acquisition, and determine the presence of learning curve in robotic spine surgery in a prospective single centre study.

Materials and methods

In a prospective study, a total of 1120 pedicle screws were placed in Freehand group (n = 175), 1250 screws were placed in fluoroscopy-assisted group (n = 172), and 1225 screws were inserted in Robotic-assisted group(n = 180). Surgical parameters and screw accuracy were analyzed between the three groups. The preoperative plan was overlapped with post operative O-arm scan to determine if the screws were executed as planned.

Results

The frequency of clinically acceptable screw placement (Gertzbein and Robbins grade A, B) in the Freehand, Fluoroscopy-assisted, and Robotic-assisted groups were 97.7 %, 98.6 %, and 99.34 % respectively. Higher pedicle screw accuracy, and lower blood loss were seen with robotic assistance. There was no significant difference in these parameters between surgeries commencing before and after 2 p.m. We found no statistically significant differences between the planned and executed screw trajectories in robotic assisted group irrespective of surgical experience.

Conclusion

The third-generation robotic-assisted pedicle screw placement system, used in conjunction with intraoperative 3D O-arm imaging, consistently lowered blood loss and increased accuracy of pedicle screw placement in the thoracolumbar spine. It also has easy adaptability into spine practice with minimal learning curve.

A novel method to evaluate the transverse pedicle angles of the lower lumbar vertebrae using digital radiography

To investigate a novel approach for establishing the transverse pedicle angle (TPA) of the lower lumbar spine using preoperative digital radiography (DR). Computed Tomography (CT) datasets of the lower lumbar were reconstructed using MIMICS 17.0 software and then imported into 3-matic software for surgical simulation and anatomical parameter measurement. A mathematical algorithm of TPA based on the Pythagorean theorem was established, and all obtained data were analyzed by SPSS software. The CT dataset from 66 samples was reconstructed as a digital model of the lower lumbar vertebrae (L3-L5), and the AP length/estimated lateral length for L3 between the right and left sides was statistically significant (P = 0.015, P = 0.005). The AP length of the right for L4 was smaller than that of the left after a paired t test was executed (P = 0.006). Both the width of the pedicle and the length of the pedicle (P2C1) were consistent with TPA (L3 Collapse

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MESH Headings

• Humans
• Lumbar Vertebrae/diagnostic imaging
• Male
• Tomography, X-Ray Computed/methods
• Female
• Middle Aged
• Adult
• Aged
• Algorithms
• Radiographic Image Enhancement/methods

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Grants

• Social Development Science and Technology Project of Shaanxi Province
• Education Department of Shaanxi Province
• Key Research and Development Projects of Shaanxi Province

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Affiliation(s)

Shixun Wu Department of Orthopedics Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China Key Laboratory of Bone Joint Disease Basic and Clinical Translation of Shaanxi Province, Xi’an, Shaanxi, China
Shizhang Liu Department of Orthopedics Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China Key Laboratory of Bone Joint Disease Basic and Clinical Translation of Shaanxi Province, Xi’an, Shaanxi, China
Ming Ling Department of Orthopedics Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China Key Laboratory of Bone Joint Disease Basic and Clinical Translation of Shaanxi Province, Xi’an, Shaanxi, China
Minggang Huang Department of Computed Tomography, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
Zhe Liu Department of Computed Tomography, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
Xianglong Duan Key Laboratory of Bone Joint Disease Basic and Clinical Translation of Shaanxi Province, Xi’an, Shaanxi, China Second Department of General Surgery, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China Institute of Medical Research, Northwestern Polytechnical University, Xi’an Shaanxi, China Second Department of General Surgery, Third Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China

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The pedicle screw accuracy using a robotic system and measured by a novel three-dimensional method

Robotics in medicine is associated with precision, accuracy, and replicability. Several robotic systems are used in spine surgery. They are all considered shared-control systems, providing "steady-hand" manipulation instruments. Although numerous studies have testified to the benefits of robotic instrumentations, they must address their true accuracy. Our study used the Mazor system under several situations and compared the spatial accuracy of the pedicle screw (PS) insertion and its planned trajectory. We used two cadaveric specimens with intact spinal structures from C7 to S1. PS planning was performed using the two registration methods (preopCT/C-arm or CT-to-fluoroscopy registration). After planning, the implant spatial orientation was defined based on six anatomic parameters using axial and sagittal CT images. Two surgical open and percutaneous access were used to insert the PS. After that, another CT acquisition was taken. Accuracy was classified into optimal, inaccurate and unacceptable according to the degree of screw deviation from its planning using the same spatial orientation method. Based on the type of spatial deviation, we also classified the PS trajectory into 16 pattern errors. Seven (19%) out of 37 implanted screws were considered unacceptable (deviation distances > 2.0 mm or angulation > 5°), and 14 (38%) were inaccurate (> 0.5 mm and ≤ 2.0 mm or > 2.5° and ≤ 5°). CT-to-fluoroscopy registration was superior to preopCT/C-arm (average deviation in 0.9 mm vs. 1.7 mm, respectively, p < 0.003), and percutaneous was slightly better than open but did not reach significance (1.3 mm vs. 1.7 mm, respectively). Regarding pattern error, the tendency was to have more axial than sagittal shifts. Using a quantitative method to categorize the screw 3D position, only 10.8% of the screws were considered unacceptable. However, with a more rigorous concept of inaccuracy, almost half were non-optimal. We also identified that, unlike some previous results, the O-arm registration delivers more accurate implants than the preopCT/C-arm method.

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

OBJECTIVE

One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.

METHODS

Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies.

RESULTS

The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated.

CONCLUSION

The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.