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KMID : 0361920220520010003
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Korean Journal of Orthodontics
2022 Volume.52 No. 1 p.3 ~ p.19
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Accuracy of one-step automated orthodontic diagnosis model using a convolutional neural network and lateral cephalogram images with different qualities obtained from nationwide multi-hospitals
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Yim Sun-Jin
Kim Sung-Chul
Kim In-Hwan
Park Jae-Woo
Cho Jin-Hyoung
Hong Mi-Hee
Kang Kyung-Hwa
Kim Min-Ji
Kim Su-Jung
Kim Yoon-Ji
Kim Young-Ho
Lim Sung-Hoon
Sung Sang-Jin
Kim Nam-Kug
Baek Seung-Hak
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Abstract
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Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals.
Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM).
Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis.
Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.
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KEYWORD
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One-step automated orthodontic diagnosis, Convolutional neural networks, Lateral cephalogram, Multi-center study
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