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Related Experiment Video

Updated: Aug 25, 2025

Precision Measurements and Parametric Models of Vertebral Endplates

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment.

Mohamad Talal Radwan¹, Çağla Sin², Nurullah Akkaya³

¹Department of Orthodontics, Faculty of Dentistry, Near East University, Mersin, Turkey.

Orthodontics & Craniofacial Research

|October 19, 2022

Summary

This study developed an artificial intelligence (AI) algorithm for accurate cervical vertebra maturation (CVM) staging, aiming to reduce human error in diagnosis. The AI demonstrated high reliability and accuracy, particularly for pre-pubertal and post-pubertal stages.

Keywords:

artificial intelligence cervical vertebrae deep learning orthodontics

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Area of Science:

Dentistry and Orthodontics
Medical Imaging Analysis
Artificial Intelligence in Healthcare

Background:

Cervical vertebra maturation (CVM) staging is crucial for assessing skeletal age and treatment planning in orthodontics.
Manual CVM assessment is subjective and prone to human error, potentially impacting treatment outcomes.

Purpose of the Study:

To develop and validate an artificial intelligence (AI) algorithm for automated and accurate CVM staging.
To minimize diagnostic errors and improve the efficiency of orthodontic treatment planning.

Main Methods:

A retrospective study included 1501 lateral cephalometric (LC) images with visible cervical vertebrae.
An AI algorithm was trained using labeled data, with an experienced orthodontist serving as the gold standard.
The algorithm's reliability was assessed using intraclass correlation coefficient (ICC) and Cohen's kappa, with data split into training, testing, and validation sets.

Main Results:

The AI segmentation network achieved a global accuracy of 0.99 and a dice score of 0.93.
The classification network demonstrated an overall accuracy of 0.802.
High inter-observer reliability (ICC=0.973, kappa=0.870) was observed, indicating excellent agreement.

Conclusions:

The developed AI algorithm reliably determines CVM stages, offering a faster and potentially more accurate diagnostic process.
Automated CVM staging can reduce human intervention and associated decision-making errors, positively impacting orthodontic treatment.
The algorithm shows particular promise for accurately identifying pre-pubertal and post-pubertal growth stages.