Details

Autor(en) / Beteiligte

• Dong, Wenxuan
• You, Meng
• He, Tao
• Dai, Jiaqi
• Tang, Yueting
• Shi, Yuchao
• Guo, Jixiang

Titel

An automatic methodology for full dentition maturity staging from OPG images using deep learning

Ist Teil von

• Applied intelligence (Dordrecht, Netherlands), 2023-12, Vol.53 (23), p.29514-29536

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Accurate maturity identification of permanent teeth for children and adolescents using orthopantomogram (OPG) images is crucial in pediatric dentistry.Although recent advancements in deep learning have shown promising outcomes in maturity staging of third molars and mandibular permanent dentition, limited research has focused on assessing the entire dentition. Furthermore, accurate performance is hindered due to variations in tooth appearance and similarities between adjacent stages. This paper develops an automatic methodology to identify the tooth maturity stages of the full permanent dentition. Specifically, a tooth localization model based on YOLOv3 is presented to detect and number each candidate permanent tooth. Subsequently, we propose a novel symmetric and ordinal staging network (SOS-Net) for the precise maturity identification of each tooth. SOS-Net incorporates a symmetric feature combination block (SFC Block) based on bilateral facial symmetry to enhance discriminative feature representation in order to improve performance, while minimizing increases in model parameters. Additionally, we introduce an auxiliary regression branch with a novel loss called adjacent stage aware (ASA) loss to provide information in the order of maturity levels and effectively reduce misclassification between adjacent stages. The proposed methodology is evaluated on a private OPG image dataset collected from individuals aged 3 to 14 years. The ablation studies and visual analysis of SOS-Net verified the effectiveness and interpretability of the SFC block and ASA loss, with a F1-score improvement of 3.77 % . Moreover, comparative experiments demonstrate the good performance of the proposed methodology compared to state-of-the-art methods in maturity staging and age estimation. Graphical abstract