Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs

Seo, Hyejun; Jeong, Tae-Sung; Shin, Jonghyun

doi:10.3390/jcm10163591

Cited by 53 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These are opposite to feature learning, which is the basic principle of deep learning. Seo et al presented a benchmarking result using six deep learning models that are variants of ResNet, GoogLenet, and MobileNet [17]. Their system is semi-automatic since it requires manual cropping of ROI as preprocessing.…”

Section: Of 12mentioning

confidence: 99%

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

Kim

et al. 2021

JCM

View full text Add to dashboard Cite

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

show abstract

Section: Of 12mentioning

confidence: 99%

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

Kim

et al. 2021

JCM

View full text Add to dashboard Cite

show abstract

“…Many scholars have tried to use CNNs for image auxiliary diagnosis in the field of orthodontics. Seo used CNNs to classify the cervical vertebral maturation stages on 600 lateral cephalometric radiographs [ 6 ]. Yoon used cascaded CNNs for landmark detection in cephalometric analyses with a database of 600 samples [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Lei

et al. 2022

Diagnostics

View full text Add to dashboard Cite

(1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns. (2) Methods: A total of 2432 lateral cephalometric radiographs were collected. They were labeled as Class I, Class II, and Class III patterns, according to their ANB angles and Wits values. The radiographs were randomly divided into the training, validation, and test sets in the ratio of 70%:15%:15%. Four different CNNs, namely VGG16, GoogLeNet, ResNet152, and DenseNet161, were trained, and their model performances were compared. (3) Results: The accuracy of the four CNNs was ranked as follows: DenseNet161 > ResNet152 > VGG16 > GoogLeNet. DenseNet161 had the highest accuracy, while GoogLeNet possessed the smallest model size and fastest inference speed. The CNNs showed better capabilities for identifying Class III patterns, followed by Classes II and I. Most of the samples that were misclassified by the CNNs were boundary cases. The activation area confirmed the CNNs without overfitting and indicated that artificial intelligence could recognize the compensatory dental features in the anterior region of the jaws and lips. (4) Conclusions: CNNs can quickly and effectively assist orthodontists in the diagnosis of sagittal skeletal classification patterns.

show abstract

“…They stated that most studied DL techniques classify CVM by focusing on a specific area (region of interest) of the cervical vertebrae. Thus, they suggested that application of high-quality input data and better-performing CNN architectures that are capable of segmenting images will help in creating models with higher performance [29].…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning to classify skeletal growth phase on 3D radiographs

Ameli

Lagravère

Lai

2023

Preprint

View full text Add to dashboard Cite

Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. The aim of this study is to develop an artificial intelligence (AI) algorithm to automatically predict the CVM stages in terms of growth phases using the cone-beam computed tomographic (CBCT) images. A total of 30,016 slices obtained from 56 patients with the age range of 7-16 years were included in the dataset. After cropping the region of interest (ROI), a convolutional neural network (CNN) was built to classify the slices based on the presence of a good vision of vertebrae for classification of the growth stages. The output was used to train another model capable of categorizing the slices into phases of growth, which were defined as Phase I (prepubertal, CVM stages 1 and 2), phase II (circumpubertal, CVM stage 3), and phase III (postpubertal, CVM stages 4, 5, and 6). After training the model, 88 unused images belonging to 3 phases were used to evaluate the performance of the model using multi-class classification metrics. The average classification accuracy of the first and second CNN-based deep learning models were 96.06% and 95.79%, respectively on the validation dataset. The multi-class classification metrics applied to the new testing dataset also showed an overall accuracy of 84% for predicting the growth phase. Moreover, phase I ranked the highest accuracy in terms of F1 score (87%), followed by phase II (83%), and phase III (80%) on new images. Our proposed models could automatically detect the C2-C4 vertebrae required for CVM staging and accurately classify slices into 3 growth phases without the need for annotating the shape and configuration of vertebrae. This will result in developing a fully automatic and less complex system with reasonable performance, comparable to expert practitioners.

show abstract

Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs

Cited by 53 publications

References 35 publications

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

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

Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks

Application of deep learning to classify skeletal growth phase on 3D radiographs

Contact Info

Product

Resources

About