ArticlePloS one2026
Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Machine-Learning-Based Prediction of Cervical Pedicle Screw Malposition from Clinical and Anatomical Features.Journal of clinical medicine · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent preoperative assessment methods for C2 pedicle screw placement face challenges including low consistency, operational complexity, and high skill demands.
objectiveThis study aimed to develop and validate a deep learning model for rapid and accurate assessment of C2 pedicle screw placement feasibility. MATERIALS AND
methodsWe developed C2-Net, an automated deep learning pipeline incorporating an image segmentation module for delineating C2 pedicles in CT images and a screw placement probability assessment module. The model's performance was evaluated using 3D-printed manually placed screws as ground truth and compared with surgeons of different experience levels.
resultsOn the test set, C2-Net achieved an accuracy of 89.4%, sensitivity of 90.0%, and specificity of 89.0%. The model demonstrated performance comparable to senior surgeons and numerically superior to junior surgeons, with higher consistency in diagnostic metrics. Attention maps generated by the model provided visual interpretation of the decision-making process. The predicted probabilities demonstrated capability in differentiating structural variations of C2 pedicles.
conclusionC2-Net shows high accuracy and efficiency in assessing C2 pedicle screw placement, outperforming junior surgeons. With its ability to provide rapid, consistent evaluations and visual interpretations, C2-Net demonstrates potential as a valuable assistive tool for clinical decision-making in spinal surgery. Trial Registration: ChiCTR2500101655.
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