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Robust and expert-level automated screw planning in the entire spine based on deep-learning [data]

Manual screw planning for spinal stabilization is a time-consuming process, interrupting the surgical workflow and prolonging operating time. Existing automated solutions focus on the lumbosacral region due to reliance on vertebral homogeneity. We introduce a deep-learning-based method reformulating...

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Bibliographic Details
Main Author: Naser, Paul (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2025-09-05
DOI:10.11588/DATA/J1DXQT
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Forschungsdaten
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Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/DATA/J1DXQT
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/DATA/J1DXQT
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Author Notes:Paul Naser
Description
Summary:Manual screw planning for spinal stabilization is a time-consuming process, interrupting the surgical workflow and prolonging operating time. Existing automated solutions focus on the lumbosacral region due to reliance on vertebral homogeneity. We introduce a deep-learning-based method reformulating screw planning as an image segmentation task, enabling automated generation of trajectories for all spinal levels and different screw types. Trained on over 450 real surgical cases, our approach achieved a 97% clinical acceptability rate on a representative testset, with a mean absolute deviation from manual planning of 2.6mm, which lies within inter- and intra-rater variability, demonstrating expert-level performance. Extensive evaluation on deformity cases and external data confirmed robust generalization across institutions, scanners, and pathologies. Importantly, this is the first automated solution for cervical lateral mass screw planning. Our method promises to streamline surgical workflows, enhance integration with 3D image navigation systems, and improve the safety and efficiency of spine surgery.
Item Description:Gesehen am 15.10.2025
Physical Description:Online Resource
DOI:10.11588/DATA/J1DXQT

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