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Precision Measurements and Parametric Models of Vertebral Endplates
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Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances.

Ya-Yun Huang1, Hong-Kai Wang2, Tsun-Kuang Chi3

  • 1Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan.

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Summary
This summary is machine-generated.

This study introduces an automated system for cervical vertebrae localization and neural foraminal distance measurement, improving diagnostic efficiency for degenerative spine conditions.

Keywords:
YOLOv8cervical spine localizationdeep learningneural foramen distance measurementspinal radiographyvertebral segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Spinal Diagnostics

Background:

  • Cervical degenerative conditions are increasing with an aging population.
  • Accurate localization of cervical vertebrae (C2-C7) is crucial for diagnosing conditions like OPLL and nerve compression.
  • Manual annotation of X-rays is time-consuming and struggles with large datasets.

Purpose of the Study:

  • To develop an automated approach for cervical vertebrae localization.
  • To measure neural foraminal distance and intervertebral space for nerve compression assessment.
  • To enhance early detection of degenerative spinal changes.

Main Methods:

  • Image enhancement techniques were applied.
  • YOLOv8 was utilized for cervical vertebrae detection and segmentation.
  • Automated analysis of neural foramen distance and intervertebral disc gaps was performed.

Main Results:

  • Achieved 99.5% accuracy in spine localization, an 11.7% improvement.
  • Reached 100% accuracy in recognizing the C7 vertebra, a 66.67% improvement over prior methods.
  • Processed each X-ray image in 17.9 milliseconds.

Conclusions:

  • The automated system significantly streamlines the diagnostic workflow.
  • This approach enhances overall diagnostic efficiency for cervical spine conditions.
  • Facilitates rapid detection of degenerative changes and aids treatment planning.