Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany.
Eur Radiol. 2022 Jul;32(7):4813-4822. doi: 10.1007/s00330-022-08582-0. Epub 2022 Mar 1.
Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinical applications. A deep neural network has been developed to automatically estimate chronological age from knee radiographs in pediatric patients.
In this retrospective study, 3816 radiographs of the knee from pediatric patients from a German population (acquired between January 2008 and December 2018) were collected to train a neural network. The network was trained to predict chronological age from the knee radiographs and was evaluated on an independent validation cohort of 423 radiographs (acquired between January 2019 and December 2020) and on an external validation cohort of 197 radiographs.
The model showed a mean absolute error of 0.86 ± 0.72 years and 0.9 ± 0.71 years on the internal and external validation cohorts, respectively. Separating age classes (< 14 years from ≥ 14 years and < 18 years from ≥ 18 years) showed AUCs between 0.94 and 0.98.
The chronological age of pediatric patients can be estimated with good accuracy from radiographs of the knee using a deep neural network.
• Radiographs of the knee can be used for age estimations in pediatric patients using a standard deep neural network. • The network showed a mean absolute error of 0.86 ± 0.72 years in an internal validation cohort and of 0.9 ± 0.71 years in an external validation cohort. • The network can be used to separate the age classes < 14 years from ≥ 14 years with an AUC of 0.97 and < 18 years from ≥ 18 years with an AUC of 0.94.
年龄估测，特别是在儿科患者中，常用于从法医、医学法律到临床应用等不同领域。已经开发出一种深度神经网络，可自动根据儿科患者的膝关节 X 光片估测实际年龄。
在这项回顾性研究中，共收集了来自德国人群的 3816 例膝关节 X 光片（2008 年 1 月至 2018 年 12 月间拍摄），用于训练神经网络。该网络被训练用于根据膝关节 X 光片预测实际年龄，并在 423 例 X 光片（2019 年 1 月至 2020 年 12 月间拍摄）的内部验证队列和 197 例 X 光片的外部验证队列上进行了评估。
该模型在内部和外部验证队列上的平均绝对误差分别为 0.86 ± 0.72 岁和 0.9 ± 0.71 岁。将年龄组（<14 岁和≥14 岁、<18 岁和≥18 岁）分开，AUC 值在 0.94 至 0.98 之间。
使用深度神经网络可以从膝关节 X 光片中准确估测儿科患者的实际年龄。
膝关节 X 光片可用于儿科患者的年龄估测，使用标准的深度神经网络。
该网络在内部验证队列中的平均绝对误差为 0.86 ± 0.72 岁，在外部验证队列中的平均绝对误差为 0.9 ± 0.71 岁。
该网络可用于将年龄组<14 岁和≥14 岁区分开来，AUC 值为 0.97，<18 岁和≥18 岁区分开来，AUC 值为 0.94。
