| ORIGINAL ARTICLE |
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| Year : 2021 | Volume
: 12
| Issue : 3 | Page : 223-227 |
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Autonomous lumbar spine pedicle screw planning using machine learning: A validation study
Kris B Siemionow1, Craig W Forsthoefel2, Michael P Foy2, Dominik Gawel1, Christian J Luciano1
1 Department of Research, Holo Surgical Inc, Chicago, IL, USA
2 Department of Orthopaedics, University of Illinois, Chicago, IL, USA
Correspondence Address:
Michael P Foy
E-270 MSS MC 844, 835 S. Wolcott Avenue, Chicago, IL
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcvjs.jcvjs_94_21
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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.
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