Li Maolin, Zhao Jiang, Liu Huanhuan, Jin Biao, Cui Xuee, Wang Dengbin
Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Radiology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China.
Insights Imaging. 2025 Aug 23;16(1):184. doi: 10.1186/s13244-025-02068-5.
Accurate age estimation is essential for assessing pediatric developmental stages and for forensics. Conventionally, pediatric age is clinically estimated by bone age through wrist X-rays. However, recent advances in deep learning enable other radiological modalities to serve as a promising complement. This study aims to explore the effectiveness of deep learning for pediatric age estimation using chest X-rays.
We developed a ResNet-based deep neural network model enhanced with Coordinate Attention mechanism to predict pediatric age from chest X-rays. A dataset comprising 128,008 images was retrospectively collected from two large tertiary hospitals in Shanghai. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed as main evaluation metrics across age groups. Further analysis was conducted using Spearman correlation and heatmap visualizations.
The model achieved an MAE of 5.86 months for males and 5.80 months for females on the internal validation set. On the external test set, the MAE was 7.40 months for males and 7.29 months for females. The Spearman correlation coefficient was above 0.98, indicating a strong positive correlation between the predicted and true age. Heatmap analysis revealed the deep learning model mainly focused on the spine, mediastinum, heart and great vessels, with additional attention given to surrounding bones.
We successfully constructed a large dataset of pediatric chest X-rays and developed a neural network model integrated with Coordinate Attention for age prediction. Experiments demonstrated the model's robustness and proved that chest X-rays can be effectively utilized for accurate pediatric age estimation.
By integrating pediatric chest X-rays with age data using deep learning, we can provide more support for predicting children's age, thereby aiding in the screening of abnormal growth and development in children.
This study explores whether deep learning could leverage chest X-rays for pediatric age prediction. Trained on over 120,000 images, the model shows high accuracy on internal and external validation sets. This method provides a potential complement for traditional bone age assessment and could reduce radiation exposure.
准确的年龄估计对于评估儿童发育阶段和法医学至关重要。传统上，儿童年龄是通过手腕X光片的骨龄进行临床估计的。然而，深度学习的最新进展使其他放射学模态成为有前景的补充方法。本研究旨在探索使用胸部X光片通过深度学习估计儿童年龄的有效性。
我们开发了一种基于ResNet的深度神经网络模型，并通过坐标注意力机制进行增强，以从胸部X光片中预测儿童年龄。从上海的两家大型三级医院回顾性收集了一个包含128,008张图像的数据集。平均绝对误差（MAE）和平均绝对百分比误差（MAPE）被用作各年龄组的主要评估指标。使用斯皮尔曼相关性和热图可视化进行了进一步分析。
该模型在内部验证集上对男性的MAE为5.86个月，对女性为5.80个月。在外部测试集上，男性的MAE为7.40个月，女性为7.29个月。斯皮尔曼相关系数高于0.98，表明预测年龄与实际年龄之间存在强正相关。热图分析显示深度学习模型主要关注脊柱、纵隔、心脏和大血管，同时也会额外关注周围骨骼。
我们成功构建了一个大型儿童胸部X光片数据集，并开发了一种集成坐标注意力的神经网络模型用于年龄预测。实验证明了该模型的稳健性，并证明胸部X光片可有效用于准确的儿童年龄估计。
通过使用深度学习将儿童胸部X光片与年龄数据相结合，我们可以为预测儿童年龄提供更多支持，从而有助于筛查儿童生长发育异常情况。
本研究探讨了深度学习是否可以利用胸部X光片进行儿童年龄预测。该模型在超过120,000张图像上进行训练，在内部和外部验证集上显示出高精度。此方法为传统骨龄评估提供了潜在补充，并可减少辐射暴露。
