Abstract
The purpose of this study is to evaluate and compare the performance of six state-of-the-art convolutional neural network (CNN)-based deep learning models for cervical vertebral maturation (CVM) on lateral cephalometric radiographs, and implement visualization of CVM classification for each model using gradient-weighted class activation map (Grad-CAM) technology. A total of 600 lateral cephalometric radiographs obtained from patients aged 6–19 years between 2013 and 2020 in Pusan National University Dental Hospital were used in this study. ResNet-18, MobileNet-v2, ResNet-50, ResNet-101, Inception-v3, and Inception-ResNet-v2 were tested to determine the optimal pre-trained network architecture. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve (AUC) values from the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models. All deep learning models demonstrated more than 90% accuracy, with Inception-ResNet-v2 performing the best, relatively. In addition, visualizing each deep learning model using Grad-CAM led to a primary focus on the cervical vertebrae and surrounding structures. The use of these deep learning models in clinical practice will facilitate dental practitioners in making accurate diagnoses and treatment plans.
Highlights
Evaluation of the growth and development of children and adolescents is important for diagnosis and treatment in the field of medicine and dentistry [1,2]
Multi-class classification metrics, accuracy (1), recall (2), precision (3), F1-score (4), and area under the curve (AUC) values from the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models
The average fication accuracy of all of convolutional neural network (CNN)-based deep learning models was over them, classification accuracy all CNN-based deep learning models was 90%
Summary
Evaluation of the growth and development of children and adolescents is important for diagnosis and treatment in the field of medicine and dentistry [1,2]. Evaluation of skeletal maturity is considered the most reliable method of determining growth and development status [2,3,4]. It aids in ascertaining the optimal time for dentofacial treatment based on skeletal maturity, and is used as a reliable indicator in forensic science and pediatric endocrinology [5,6]. Hand–wrist radiograph analysis is considered to be the gold standard to evaluate skeletal maturity [7]. The evaluation of bone age using hand–wrist radiographs has the advantage of being able to evaluate the ossification onset of the ulnar sesamoid through the different types of bones detected in the area; it is widely used in the medical field [8,9]
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