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Automatic Pavlov ratio measurement method based on spinal landmarks identification by a deep-learning model.

Yongli Wang1,2, Chi Huang1, Junhao Zhou1

  • 1Second Affiliated Hospital (Changzheng Hospital) of Naval Medical University, Shanghai, China.

Medical Physics
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model to automatically measure the Pavlov ratio from cervical spine X-rays, improving diagnostic accuracy for cervical spondylosis and reducing observer variability in measurements.

Keywords:
Pavlov ratioautomatic measurementdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Spinal Diagnostics

Background:

  • Cervical canal stenosis is a key factor in cervical spondylosis.
  • Accurate Pavlov ratio measurement is vital for diagnosing and treating cervical spinal stenosis.
  • Manual measurements are subjective and inefficient, impacting clinical evaluation.

Purpose of the Study:

  • To develop a deep learning model for automatic and accurate Pavlov ratio measurement.
  • To detect keypoints on cervical spine radiographs for precise ratio calculation.
  • To enhance the diagnostic workflow for cervical spinal stenosis.

Main Methods:

  • A two-stage deep learning model combining YOLOX for region detection and HRNet with deconvolutional networks for keypoint identification.
  • Detection of 38 keypoints on plain lateral cervical spine radiographs.
  • Training and validation using datasets from multiple hospitals.

Main Results:

  • The model achieved high accuracy in landmark recognition (MAE 0.05-0.08, SMAPE 4.54%-6.43%).
  • Performance is comparable to experienced clinicians and superior to junior physicians.
  • Excellent accuracy was confirmed in external validation datasets (SMAPE 4.40%-5.95%).

Conclusions:

  • A novel YOLOX-HRNet-DN model accurately identifies landmarks and measures the Pavlov ratio on cervical spine radiographs.
  • This automated method offers a potential tool to improve the efficiency and precision of cervical spondylosis diagnosis and treatment.
  • The model demonstrates significant potential for clinical application in spinal diagnostics.