ArticleGlobal spine journal2025
Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset.
Article in Global spine journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A Proof-of-Concept Study.Neurospine · 2026Article
- Automated Metrics for the Diagnosis of Instability Between the 2nd and 7th Cervical Vertebrae.Bioengineering (Basel, Switzerland) · 2026Article
- Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.NPJ digital medicine · 2026Article
- Simulating Federated Learning to Enable Multi-Hospital Collaboration for Lumbopelvic Alignment Estimation.JOR spine · 2025Article
- Automated radiographic assessment of lower limb alignment using deep learning in a data-constrained clinical setting.BMC musculoskeletal disorders · 2025Article
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Authors and funding
11 authors.
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Abstract
STUDY
designRetrospective data analysis.
objectivesThis study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs.
methodsWe collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA).
resultsRegarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively.
conclusionsIn this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution.
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