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BMC Oral Health
Published in: BMC Oral Health 1/2023

Open Access 01-12-2023 | Artificial Intelligence | Research

The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning

Authors: Hairui Li, Haizhen Li, Lingjun Yuan, Chao Liu, Shengzhao Xiao, Zhen Liu, Guoli Zhou, Ting Dong, Ningjuan Ouyang, Lu Liu, Chenglong Ma, Yang Feng, Youyi Zheng, Lunguo Xia, Bing Fang

Published in: BMC Oral Health | Issue 1/2023

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Abstract

Background

Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination.

Methods

This study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images.

Results

The system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2.

Conclusions

The results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.
Appendix
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Metadata
Title
The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning
Authors
Hairui Li
Haizhen Li
Lingjun Yuan
Chao Liu
Shengzhao Xiao
Zhen Liu
Guoli Zhou
Ting Dong
Ningjuan Ouyang
Lu Liu
Chenglong Ma
Yang Feng
Youyi Zheng
Lunguo Xia
Bing Fang
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-023-03266-7

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