Abstract：
Bone age determination in individuals is important for the diagnosis and treatment of growing children. This study aimed to develop a deep-learning model for bone age estimation using lateral cephalometric radiographs (LCRs) and regions of interest (ROIs) in growing children and evaluate its performance. This retrospective study included 1050 patients aged 4-18 years who underwent LCR and hand-wrist radiography on the same day at Pusan National University Dental Hospital and Ulsan University Hospital between January 2014 and June 2023. Two pretrained convolutional neural networks, InceptionResNet-v2 and NasNet-Large, were employed to develop a deep-learning model for bone age estimation. The LCRs and ROIs, which were designated as the cervical vertebrae areas, were labeled according to the patient\'s bone age. Bone age was collected from the same patient\'s hand-wrist radiograph. Deep-learning models trained with five-fold cross-validation were tested using internal and external validations. The LCR-trained model outperformed the ROI-trained models. In addition, visualization of each deep learning model using the gradient-weighted regression activation mapping technique revealed a difference in focus in bone age estimation. The findings of this comparative study are significant because they demonstrate the feasibility of bone age estimation via deep learning with craniofacial bones and dentition, in addition to the cervical vertebrae on the LCR of growing children.
摘要：
确定个体的骨龄对于诊断和治疗成长中的儿童很重要。这项研究旨在开发一种深度学习模型，用于在成长中的儿童中使用侧位头颅X光片（LCR）和感兴趣区域（ROIs）进行骨龄估计，并评估其性能。这项回顾性研究包括2014年1月至2023年6月在釜山国立大学牙科医院和蔚山大学医院同一天接受LCR和腕部X线检查的1050名4-18岁患者。两个预训练的卷积神经网络，InceptionResNet-v2和NasNet-Large，用于开发骨龄估计的深度学习模型。LCR和ROI,被指定为颈椎区域，根据患者的骨龄进行标记。从同一患者的手腕部X光片收集骨龄。使用内部和外部验证测试了经过五次交叉验证训练的深度学习模型。LCR训练的模型优于ROI训练的模型。此外,使用梯度加权回归激活映射技术对每个深度学习模型进行可视化，揭示了骨龄估计的焦点差异。这项比较研究的结果很重要，因为它们证明了通过颅面骨骼和牙列进行深度学习来估算骨骼年龄的可行性。除了颈椎上LCR的成长中的儿童。
