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Deep learning based quantitative cervical vertebral maturation analysis.

Fulin Jiang1, Abbas Ahmed Abdulqader2, Yan Yan2

  • 1College of Computer Science, Chongqing University, Chongqing University Three Gorges Hospital, Chongqing, 400044, China.

Head & Face Medicine
|March 27, 2025
PubMed
Summary

This study introduces a two-stage neural network for automated cervical vertebral maturation (QCVM) staging. The system improves landmark localization accuracy and aids junior orthodontists in staging, enhancing clinical diagnostics.

Keywords:
Artificial intelligenceAutomated landmark locationLateral cephalogramOrthodonticsQuantitative cervical vertebral maturation (QCVM)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Orthodontics

Background:

  • Quantitative cervical vertebral maturation (QCVM) staging is crucial for orthodontic treatment planning.
  • Current methods for landmark identification and maturation staging are often subjective and time-consuming.
  • Deep learning models show promise in handling anatomical variations in cephalometric analysis.

Purpose of the Study:

  • To develop an advanced two-stage convolutional neural network (CVnet) for precise cervical vertebral landmark localization.
  • To enhance the accuracy of quantitative cervical vertebral maturation (QCVM) staging.
  • To evaluate the system's performance and its impact on the diagnostic accuracy of junior orthodontists.

Main Methods:

  • A two-stage convolutional neural network (CVnet) was designed and trained on 2100 cephalometric images (8:1:1 ratio for training, validation, testing).
  • The system evaluated various region of interest (ROI) sizes to locate 19 cervical vertebral landmarks and classify QCVM stages.
  • Landmark localization accuracy was measured by success detection rate and student t-test; QCVM diagnostic accuracy was tested with six junior orthodontists.

Main Results:

  • Optimal ROI calibration yielded an average landmark localization error of 0.66 ± 0.46 mm with a 98.10% success detection rate within 2 mm.
  • The system achieved a QCVM stage identification accuracy of 69.52%.
  • Junior orthodontists showed a 10.95% improvement in staging accuracy when using the system.

Conclusions:

  • A two-stage neural network effectively automates cervical vertebral landmark identification and QCVM staging.
  • The developed method streamlines the workflow and improves skeletal maturation estimation accuracy.
  • This system provides valuable clinical support for consistent treatment planning, especially for less experienced practitioners.