ArticleJournal of bone oncology2024
Automated measurement of lumbar pedicle screw parameters using deep learning algorithm on preoperative CT scans.
Article in Journal of bone oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- Preoperative evaluation of C2 pedicle screw placement using a deep learning model: Development and validation study.PloS one · 2026Article
- Evaluation of Pedicle Screw Position on Computerized Tomography Using Three-Dimensional Reconstruction Software.Medicina (Kaunas, Lithuania) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aims to devise and assess an automated measurement framework for lumbar pedicle screw parameters leveraging preoperative computed tomography (CT) scans and a deep learning algorithm. Methods: A deep learning model was constructed employing a dataset comprising 1410 axial preoperative CT images of lumbar pedicles sourced from 282 patients. The model was trained to predict several screw parameters, including the axial angle and width of pedicles, the length of pedicle screw paths, and the interpedicular distance. The mean values of these parameters, as determined by two radiologists and one spinal surgeon, served as the reference standard. Results: The deep learning model achieved high agreement with the reference standard for the axial angle of the left pedicle (ICC = 0.92) and right pedicle (ICC = 0.93), as well as for the length of the left pedicle screw path (ICC = 0.82) and right pedicle (ICC = 0.87). Similarly, high agreement was observed for pedicle width (left ICC = 0.97, right ICC = 0.98) and interpedicular distance (ICC = 0.91). Overall, the model's performance paralleled that of manual determination of lumbar pedicle screw parameters. Conclusion: The developed deep learning-based model demonstrates proficiency in accurately identifying landmarks on preoperative CT scans and autonomously generating parameters relevant to lumbar pedicle screw placement. These findings suggest its potential to offer efficient and precise measurements for clinical applications.
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