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Related Concept Videos

Aggregates Classification01:29

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Related Experiment Video

Updated: Jun 16, 2026

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

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Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach.

Parisa Motie1, Ali Ashkan2, Hossein Mohammad-Rahimi3,4

  • 1Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran.

Imaging Science in Dentistry
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an automated framework for classifying cervical vertebral maturation (CVM) stages, achieving promising accuracy. This AI-driven approach aids in predicting growth patterns for orthodontic treatment planning.

Keywords:
Artificial IntelligenceCervical VertebraeClassificationDeep LearningGrowth

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Area of Science:

  • Orthodontics and Dental Imaging
  • Artificial Intelligence in Healthcare
  • Biometric Analysis

Background:

  • Accurate classification of cervical vertebral maturation (CVM) stages is crucial for predicting growth spurts and patterns in orthodontics.
  • Manual CVM assessment relies on subjective interpretation of lateral cephalograms, necessitating objective and automated methods.
  • Developing an automated system can improve efficiency and consistency in orthodontic diagnostics.

Purpose of the Study:

  • To develop and evaluate a multistage, automated framework for classifying cervical vertebral maturation (CVM) stages.
  • To enhance the precision and reliability of CVM assessment using deep learning models.
  • To provide a tool for more accurate prediction of growth rates and patterns in orthodontic patients.

Main Methods:

  • A dataset of 2325 lateral cephalograms was utilized, with expert classification into 6 CVM stages.
  • A two-stage deep learning approach was implemented: object detection (Faster RCNN) for region extraction and classification (ResNet 101) for CVM staging.
  • Models were trained and validated using 10-fold cross-validation, with visualization of learning processes via gradient-weighted class activation maps.

Main Results:

  • The overall automated framework achieved a promising accuracy of 82.96% for CVM classification.
  • Object detection for region-of-interest extraction demonstrated high performance with mAP50 and mAP75 values of 100%.
  • The initial classification model distinguishing between CS1-CS3 and CS4-CS6 stages reached 99.10% accuracy; subsequent sub-classification showed accuracies of 86.49% (CS1-CS3) and 82.80% (CS4-CS6).

Conclusions:

  • The developed fully automated, multistage framework for CVM classification demonstrates promising accuracy.
  • This AI-driven approach offers a reliable and efficient alternative to manual CVM assessment.
  • Further refinement of the automated framework could significantly benefit orthodontic treatment planning and growth prediction.