Gao Lu, Chen Zhiyu, Zang Lin, Sun Zhipeng, Wang Qing, Yu Guoxia
Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
School of Software Engineering, North University of China, Taiyuan 030051, China.
Bioengineering (Basel). 2022 Jul 14;9(7):316. doi: 10.3390/bioengineering9070316.
Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status.
We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years old. The midpalatal suture region of interest (ROI) labeling was completed by two experienced clinical experts. The CBCT image fusion algorithm and image texture feature analysis algorithm were constructed and optimized. The age range prediction convolutional neural network (CNN) was conducted and tested.
The midpalatal suture fusion images contain complete semantic information for appraising midpalatal suture maturation and ossification status during the fast growth and development period. Correlation and homogeneity are the two texture features with the strongest relevance to chronological age. The overall performance of the age range prediction CNN model is satisfactory, especially in the 4 to 10 years range and the 17 to 23 years range, while for the 13 to 14 years range, the model performance is compromised.
The image fusion algorithm can help show the overall perspective of the midpalatal suture in one fused image effectively. Furthermore, clinical decisions for maxillary transverse deficiency should be appraised by midpalatal suture image features directly rather than by age, especially in the 13 to 14 years range.
腭中缝成熟度和骨化状态是评估上颌横向发育状态的基础。
我们建立了一个生长人群的腭中缝锥形束计算机断层扫描（CBCT）标准化数据库，包括来自690名24岁以下参与者的1006份CBCT文件。腭中缝感兴趣区域（ROI）标注由两位经验丰富的临床专家完成。构建并优化了CBCT图像融合算法和图像纹理特征分析算法。进行并测试了年龄范围预测卷积神经网络（CNN）。
腭中缝融合图像包含了在快速生长发育期评估腭中缝成熟度和骨化状态的完整语义信息。相关性和同质性是与实际年龄相关性最强的两个纹理特征。年龄范围预测CNN模型的整体性能令人满意，尤其是在4至10岁范围和17至23岁范围，而在13至14岁范围，模型性能有所下降。
图像融合算法可以有效地在一幅融合图像中帮助显示腭中缝的整体情况。此外，上颌横向发育不足的临床决策应直接通过腭中缝图像特征进行评估，而不是通过年龄，尤其是在13至14岁范围。
