Korean J Orthod.  2022 Mar;52(2):112-122. 10.4041/kjod.2022.52.2.112.

Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study

• MohammadRahimi H
• Motamadian SR
• Nadimi M
• HassanzadehSamani S
• Minabi MAS
• Mahmoudinia E
• Y. Lee V
• Rohban MH

• KMID: 2527455
• DOI: http://doi.org/10.4041/kjod.2022.52.2.112

Abstract

Objective
This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs.
Methods
The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses.
Results
The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model.
Conclusions
The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

Keyword

Computer algorithm; Growth evaluation; Cervical vertebrae; Artificial intelligence

Figure

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  Figure 1 Fifty epochs of ResNet-101 training. A, Changes in accuracy during training in both the validation and training sets in the six-class classification. B, Changes in loss during training in both the validation and training sets in the six-class classification. C, Changes in accuracy during training in both the validation and training sets in the three-class classification. D, Changes in loss during training in both the validation and training sets in the three-class classification. The epochs used for the early stopping strategy (to avoid overfitting) are shown as red dotted lines.

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  Figure 2 A, Confusion matrix of the test set in the six-class classification of ResNet-101. B, Confusion matrix of the test set in the three-class classification of ResNet-101.

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  Figure 3 A, Receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) score in the test set in the six-class classification of ResNet-101. B, ROC curve and the AUC score in the test set in the three-class classification of ResNet-101.

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  Figure 4 The structure of ResNet-101. Conv, convolution; Avg, average.

Reference

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