Abstract
Introduction: Several techniques for pedicle screw placement have been described including freehand techniques, fluoroscopy assisted, computed tomography (CT) guidance, and robotics. Image-guided surgery offers the potential to combine the benefits of CT guidance without the added radiation. This study investigated the ability of a neural network to place lumbar pedicle screws with the correct length, diameter, and angulation autonomously within radiographs without the need for human involvement. Materials and Methods: The neural network was trained using a machine learning process. The method combines the previously reported autonomous spine segmentation solution with a landmark localization solution. The pedicle screw placement was evaluated using the Zdichavsky, Ravi, and Gertzbein grading systems. Results: In total, the program placed 208 pedicle screws between the L1 and S1 spinal levels. Of the 208 placed pedicle screws, 208 (100%) had a Zdichavsky Score 1A, 206 (99.0%) of all screws were Ravi Grade 1, and Gertzbein Grade A indicating no breech. The final two screws (1.0%) had a Ravi score of 2 (<2 mm breech) and a Gertzbein grade of B (<2 mm breech). Conclusion: The results of this experiment can be combined with an image-guided platform to provide an efficient and highly effective method of placing pedicle screws during spinal stabilization surgery.
Original language | English (US) |
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Pages (from-to) | 223-227 |
Number of pages | 5 |
Journal | Journal of Craniovertebral Junction and Spine |
Volume | 12 |
Issue number | 3 |
DOIs | |
State | Published - Jul 1 2021 |
Externally published | Yes |
Keywords
- Augmented reality
- computed tomography radiography
- fusion
- lumbar spine
- machine learning
- pedicle placement
ASJC Scopus subject areas
- Surgery
- Clinical Neurology