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Updated: Jul 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

Hairui Li1, Haizhen Li1, Lingjun Yuan1

  • 1Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

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|August 12, 2023
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Summary

A new deep learning system automates cervical vertebral maturation (CVM) assessment, improving accuracy and consistency. This AI tool aids clinicians in determining growth periods and optimizing treatment timing for better patient outcomes.

Keywords:
Cervical vertebral maturation (CVM) assessmentDeep learningLateral cephalogram

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Area of Science:

  • Utilizes artificial intelligence and deep learning for medical image analysis.
  • Focuses on quantitative assessment in orthodontics and pediatric dentistry.

Background:

  • Cervical vertebral maturation (CVM) is crucial for predicting growth and guiding treatment timing.
  • Current CVM assessment relies heavily on clinician experience, leading to variability.
  • The need for an objective and automated system for CVM assessment is evident.

Purpose of the Study:

  • To develop a fully automated, high-accuracy deep learning system for CVM assessment.
  • To provide a reliable tool for determining growth periods and informing treatment decisions.

Main Methods:

  • A deep learning system, psc-CVM, was trained on 10,200 lateral cephalograms.
  • The system comprises three networks: Position, Shape Recognition, and CVM Assessment.
  • Performance was evaluated against an expert panel using statistical measures like AUC, Kappa, and ICC.

Main Results:

  • The psc-CVM system achieved an average AUC of 0.94 and 70.42% accuracy on the test set.
  • High agreement with expert panels was observed, with weighted Kappa of 0.844 and ICC of 0.946.
  • The system demonstrated strong performance across different cervical vertebral maturation stages.

Conclusions:

  • The automated psc-CVM system demonstrates high accuracy and consistency in CVM assessment.
  • This AI-driven tool serves as an efficient, stable diagnostic aid for clinicians.
  • It offers valuable support for determining growth and developmental stages via CVM.