Development and validation of an automated planning tool for navigated lumbosacral pedicle screws using a convolutional neural network
Background context - Navigation and robotic systems have been increasingly applied to spinal instrumentation but dedicated screw planning is a time-consuming prerequisite to tap the full potential of these techniques. - Purpose - To develop and validate an automated planning tool for lumbosacral ped...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
[October 2022]
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| In: |
The spine journal
Year: 2022, Jahrgang: 22, Heft: 10, Pages: 1666-1676 |
| ISSN: | 1878-1632 |
| DOI: | 10.1016/j.spinee.2022.05.002 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.spinee.2022.05.002 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S1529943022001930 
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| Verfasserangaben: | Moritz Scherer, Lisa Kausch, Basem Ishak, Tobias Norajitra, Philipp Vollmuth, Karl Kiening, Andreas Unterberg, Klaus Maier-Hein, Jan-Oliver Neumann |
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| 245 | 1 | 0 | |a Development and validation of an automated planning tool for navigated lumbosacral pedicle screws using a convolutional neural network |c Moritz Scherer, Lisa Kausch, Basem Ishak, Tobias Norajitra, Philipp Vollmuth, Karl Kiening, Andreas Unterberg, Klaus Maier-Hein, Jan-Oliver Neumann |
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| 520 | |a Background context - Navigation and robotic systems have been increasingly applied to spinal instrumentation but dedicated screw planning is a time-consuming prerequisite to tap the full potential of these techniques. - Purpose - To develop and validate an automated planning tool for lumbosacral pedicle screw placement using a convolutional neural network (CNN) to facilitate the planning process. - Study Design/Setting - Retrospective analysis and processing of CT and screw planning data randomly selected from a consecutive registry of CT-navigated instrumentations from a single academic institution. - Patient Sample - Data from 179 cases was processed for CNN training and validation (155 for training, 24 for validation) leveraging a total of 1182 screws (1052 for training, 130 for validation). - Outcome Measures - Quantitative and qualitative (Gertzbein-Robbins classification [GR]) validation via comparison of automatically and manually planned reference screws, inter-rater and intra-rater variability. - Methods - Annotated data from CT-navigated instrumentation was used to train a CNN operating in a vertebra instance-based approach employing a state-of-the-art U-Net framework. Internal five-fold cross-validation and external validation on an independent cohort not previously involved in training was performed. Quantitative validation of automatically planned screws was performed in comparison to corresponding manually planned screws by calculating the minimal absolute difference (MAD) of screw head and tip points, length and diameter, screw direction and Dice coefficient. Results were evaluated in relation to inter-rater and intra-rater variability of manual screw planning. - Results - Automated screw planning was successful in all targeted 130 screws. Compared with manually planned screws as a reference, mean MAD of automatically planned screws was 4.61±2.27 mm for screw head, 3.96±2.19 mm for tip points and 5.51±3.64° for screw direction. These differences were either statistically comparable or significantly smaller when compared with interrater variability of manual screw planning (p>.99 for head point and direction, p=.004 for tip point, respectively). Mean Dice coefficient of 0.61±0.16 indicated significantly greater agreement of automatic screws with the manual reference compared with interrater agreement (Dice 0.56±0.18, p<.001). Automatically planned screws were marginally shorter (MAD 3.4±3.2 mm) and thinner (MAD mean 0.3±0.6 mm) compared with the manual reference, but with statistical significance (p<.0001, respectively). Automatically planned screws were GR grade A in 96.2% in qualitative validation. Planning time was significantly shorter with the automatic approach (0:41 min vs. 6:41 min, p<.0001). - Conclusions - We derived and validated a fully automated planning tool for lumbosacral pedicle screws using a CNN. Our validation showed noninferiority to manual screw planning and provided sufficient accuracy to facilitate and expedite the screw planning process. These results offer a high potential to improve workflows in spine surgery when integrated into navigation or robotic assistance systems. | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Convolutional neural network | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Gertzbein-Robbins classification | |
| 650 | 4 | |a Pedicle screw | |
| 650 | 4 | |a Robotic-assisted surgery | |
| 650 | 4 | |a Spinal fusion | |
| 650 | 4 | |a Spinal instrumentation | |
| 650 | 4 | |a Spinal navigation | |
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