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Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.

Eun-Gyeong Kim¹, Il-Seok Oh¹, Jeong-Eun So¹

¹Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54907, Korea.

Journal of Clinical Medicine

|November 27, 2021

Summary

This study introduces deep learning models for estimating cervical vertebral maturation (CVM) from lateral cephalograms. A three-step segmentation model achieved the highest accuracy, outperforming non-segmentation approaches.

Keywords:

bone maturation cervical vertebrae maturation deep learning lateral cephalogram

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Precision Measurements and Parametric Models of Vertebral Endplates

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Published on: September 17, 2019

Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Deep learning is increasingly used for bone maturation estimation.
Most studies focus on hand-wrist radiographs, with limited research on cervical vertebral maturation (CVM) using lateral cephalograms.

Purpose of the Study:

To propose and evaluate deep learning models for estimating CVM from lateral cephalograms.
To develop a stepwise segmentation-based model focusing on the C2-C4 cervical vertebral regions.

Main Methods:

Three convolutional neural network (CNN) models were developed: one-step CVM classification, two-step ROI detection and CVM classification, and three-step ROI detection, cervical segmentation, and CVM classification.
The dataset comprised 600 lateral cephalogram images across six classes.

Main Results:

The three-step segmentation-based model demonstrated the highest accuracy at 62.5%.
This segmentation approach outperformed models that did not incorporate segmentation.

Conclusions:

Deep learning models, particularly a three-step segmentation-based approach, show promise for accurate CVM estimation from lateral cephalograms.
Focusing on specific cervical vertebral regions (C2-C4) improves model performance.