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Related Experiment Video

Updated: May 9, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A

Shun Yamamoto1, Yutaro Fuse1,2, Yoshitaka Nagashima1

  • 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Japan.

Neurospine
|May 7, 2026
PubMed
Summary

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This summary is machine-generated.

A new deep learning model accurately measures the occipito-axial (O-C2) angle using cervical spine X-rays. This automated method offers rapid and reliable O-C2 angle assessment for clinical practice.

Area of Science:

  • Neurosurgery
  • Radiology
  • Artificial Intelligence

Background:

  • Maintaining the occipito-axial (O-C2) angle is critical after occipitocervical fusion to prevent complications.
  • Current automated O-C2 angle measurement methods lack the speed required for routine clinical practice.

Purpose of the Study:

  • To develop a deep learning model utilizing the YOLO object detection algorithm for automated identification of anatomical landmarks.
  • To enable rapid and accurate calculation of the O-C2 angle from lateral cervical radiographs.

Main Methods:

  • A retrospective analysis of 574 lateral cervical radiographs from an internal dataset and 100 from an external dataset.
  • Development of a YOLO-based deep learning model for landmark detection (occipital bone, C2 corners, hard palate).
Keywords:
AlgorithmsCervical vertebraeDeep learningRadiography

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Last Updated: May 9, 2026

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  • Evaluation of model performance against manual measurements by three expert raters.

    • Main Results:

    • The model achieved high detection performance for all landmarks (F1 scores ≥ 0.97).
    • O-C2 angle estimation showed a mean absolute error of 2.35° and root mean squared error of 2.98°.
    • The model achieved 94.7% accuracy in detecting all four landmarks simultaneously, with a rapid inference time of ~0.14 seconds per image.

    Conclusions:

    • The developed deep learning model provides rapid and accurate O-C2 angle measurements from lateral cervical radiographs.
    • The model's performance is comparable to expert raters, indicating significant clinical utility for routine practice.