Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs

BMC Oral Health

Research

Jia-Nan Zhang¹  Cheng-Yi Huang¹  Qiong Wang¹  Feng-Yang Yu²  Chong Zhong²  Hai-Ping Lu³  Si Chen⁴  Jia Hou⁵ 

[1] Center of Orthodontics, Department of Dentistry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3# Qingchundong Road, Hangzhou, China;Center of Orthodontics, Perfect Dental Care, 108# Xintang Road, Hangzhou, China;Department of Orthodontics, College of Stomatology, Zhejiang Chinese Medical University, 548# Binwen Road, Hangzhou, China;Department of Orthodontics, Peking University School and Hospital of Stomatology, 22# Zhongguancun S. Ave., Beijing, China;School of Automation, Lishui Institute, Hangzhou Dianzi University, 1158# 2nd Street, Hangzhou, China;

关键词: Deep learning;    Mandibular growth;    Prediction;    Anterior crossbite;    Convolutional neural networks;   

DOI  :  10.1186/s12903-023-02734-4

 received in 2022-11-03, accepted in 2023-01-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundIt is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into normal or overdeveloped using cephalometric radiographs.MethodsA deep convolutional neural network (CNN) model was constructed based on the algorithm ResNet50 and trained on the basis of 256 cephalometric radiographs. The prediction behavior of the model was tested on 40 cephalograms and visualized by equipped with Grad-CAM. The prediction performance of the CNN model was compared with that of three junior orthodontists.ResultsThe deep-learning model showed a good prediction accuracy about 85%, much higher when compared with the 54.2% of the junior orthodontists. The sensitivity and specificity of the model was 0.95 and 0.75 respectively, higher than that of the junior orthodontists (0.62 and 0.47 respectively). The area under the curve value of the deep-learning model was 0.9775. Visual inspection showed that the model mainly focused on the characteristics of special regions including chin, lower edge of the mandible, incisor teeth, airway and condyle to conduct the prediction.ConclusionsThe deep-learning CNN model could predict the growth trend of the mandible in anterior crossbite children with relatively high accuracy using cephalometric images. The deep learning model made the prediction decision mainly by identifying the characteristics of the regions of chin, lower edge of the mandible, incisor teeth area, airway and condyle in cephalometric images.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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