Haghnegahdar Abdolaziz, Pakshir Hamid Reza, Zandieh Mojtaba, Ghanbari Ilnaz
Dept. of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
Dept. of Orthodontics, Orthodontic Research Center, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
J Dent (Shiraz). 2024 Mar 1;25(1):51-58. doi: 10.30476/dentjods.2023.95629.1882. eCollection 2024 Mar.
Bone age is a more accurate assessment for biologic development than chronological age. The most common method for bone age estimation is using Pyle and Greulich Atlas. Today, computer-based techniques are becoming more favorable among investigators. However, the morphological features in Greulich and Pyle method are difficult to be converted into quantitative measures. During recent years, metacarpal bones and metacarpophalangeal joints dimensions were shown to be highly correlated with skeletal age.
In this study, we have evaluated the accuracy and reliability of a trained neural network for bone age estimation with quantitative and recently introduced related data, including chronological age, height, trunk height, weight, metacarpal bones, and metacarpophalangeal joints dimensions.
In this cross sectional retrospective study, aneural network, using MATLAB, was utilized to determine bone age by employing quantitative features for 304 subjects. To evaluate the accuracy of age estimation software, paired t-test, and inter-class correlation was used.
The difference between the mean bone ages determined by the radiologists and the mean bone ages assessed by the age estimation software was not significant ( Value= 0.119 in male subjects and = 0.922 in female subjects). The results from the software and radiologists showed a strong correlation -ICC=0.990 in male subjects and ICC=0.986 in female subjects (< 0.001).
The results have shown an acceptable accuracy in bone age estimation with training neural network and using dimensions of bones and joints.
骨龄是比实际年龄更准确的生物发育评估指标。最常用的骨龄评估方法是使用派尔和格伦利希图谱。如今，基于计算机的技术在研究人员中越来越受欢迎。然而，格伦利希和派尔方法中的形态特征难以转化为定量指标。近年来，掌骨和掌指关节尺寸被证明与骨骼年龄高度相关。
在本研究中，我们使用包括实际年龄、身高、躯干高度、体重、掌骨和掌指关节尺寸等定量及最近引入的相关数据，评估了经过训练的神经网络用于骨龄估计的准确性和可靠性。
在这项横断面回顾性研究中，使用MATLAB的神经网络利用304名受试者的定量特征来确定骨龄。为评估年龄估计软件的准确性，采用了配对t检验和组内相关分析。
放射科医生确定的平均骨龄与年龄估计软件评估的平均骨龄之间的差异不显著（男性受试者t值 = 0.119，女性受试者t值 = 0.922）。软件和放射科医生的结果显示出很强的相关性——男性受试者组内相关系数ICC = 0.990，女性受试者ICC = 0.986（P < 0.001）。
结果表明，使用训练后的神经网络并结合骨骼和关节尺寸进行骨龄估计具有可接受的准确性。
