First reported use of real-time intraoperative computed tomography angiography image registration using the Machine-vision Image Guided Surgery system: illustrative case

Pedicle screw accuracy placed with assistance of machine vision technology in patients with neuromuscular scoliosis

INTRODUCTION

Pedicle screws are the primary method of vertebral fixation in scoliosis surgery, but there are lingering concerns over potential malposition. The rates of pedicle screw malposition in pediatric spine surgery vary from 10% to 21%. Malpositioned screws can lead to potentially catastrophic neurological, vascular, and visceral complications. Pedicle screw positioning in patients with neuromuscular scoliosis is challenging due to a combination of large curves, complex pelvic anatomy, and osteopenia. This study aimed to determine the rate of pedicle screw malposition, associated complications, and subsequent revision from screws placed with the assistance of machine vision navigation technology in patients with neuromuscular scoliosis undergoing posterior instrumentation and fusion.

METHOD

A retrospective analysis of the records of patients with neuromuscular scoliosis who underwent thoracolumbar pedicle screw insertion with the assistance of machine-vision image guidance navigation was performed. Screws were inserted by either a staff surgeon, orthopaedic fellow, or orthopaedic resident. Post-operative ultra-low dose CT scans were used to assess pedicle screw accuracy. The Gertzbein classification was used to grade any pedicle breaches (grade 0, no breach; grade 1, <2 mm; grade 2, 2-4 mm; grade 3, >4 mm). A screw was deemed accurate if no breach was identified (grade 0).

RESULTS

25 patients were included in the analysis, with a mean age of 13.6 years (range 11 to 18 years; 13/25 (52.0%) were female. The average pre-operative supine Cobb angle was 90.0 degrees (48-120 degrees). A total of 687 screws from 25 patients were analyzed (402 thoracic, 241 lumbosacral, 44 S2 alar-iliac (S2AI) screws). Surgical trainees (fellows and orthopaedic residents) inserted 46.6% (320/687) of screws with 98.8% (4/320) accuracy. The overall accuracy of pedicle screw insertion was 98.0% (Grade 0, no breach). All 13 breaches that occurred in the thoracic and lumbar screws were Grade 1. Of the 44 S2AI screws placed, one screw had a Grade 3 breach (2.3%) noted on intra-operative radiographs following rod placement and correction. This screw was subsequently revised. None of the breaches resulted in neuromonitoring changes, vessel, or visceral injuries.

CONCLUSION

Machine vision navigation technology combined with careful free-hand pedicle screw insertion techniques demonstrated high levels of pedicle screw insertion accuracy, even in patients with challenging anatomy.

Image-Guided Navigation in Spine Surgery: From Historical Developments to Future Perspectives

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

Development of a Robotic Spine Surgery Program: Rationale, Strategy, Challenges, and Monitoring of Outcomes After Implementation

Surgical robots were invented in the 1980s, and since then, robotic-assisted surgery has become commonplace. In the field of spine surgery, robotic assistance is utilized mainly to place pedicle screws, and multiple studies have demonstrated that robots can increase the accuracy of screw placement and reduce radiation exposure to the patient and the surgeon. However, this may be at the cost of longer operative times, complications, and the risk of errors in mapping the patient's anatomy.

SpineDepth: A Multi-Modal Data Collection Approach for Automatic Labelling and Intraoperative Spinal Shape Reconstruction Based on RGB-D Data

Computer aided orthopedic surgery suffers from low clinical adoption, despite increased accuracy and patient safety. This can partly be attributed to cumbersome and often radiation intensive registration methods. Emerging RGB-D sensors combined with artificial intelligence data-driven methods have the potential to streamline these procedures. However, developing such methods requires vast amount of data. To this end, a multi-modal approach that enables acquisition of large clinical data, tailored to pedicle screw placement, using RGB-D sensors and a co-calibrated high-end optical tracking system was developed. The resulting dataset comprises RGB-D recordings of pedicle screw placement along with individually tracked ground truth poses and shapes of spine levels L1-L5 from ten cadaveric specimens. Besides a detailed description of our setup, quantitative and qualitative outcome measures are provided. We found a mean target registration error of 1.5 mm. The median deviation between measured and ground truth bone surface was 2.4 mm. In addition, a surgeon rated the overall alignment based on 10% random samples as 5.8 on a scale from 1 to 6. Generation of labeled RGB-D data for orthopedic interventions with satisfactory accuracy is feasible, and its publication shall promote future development of data-driven artificial intelligence methods for fast and reliable intraoperative registration.