Hong Juntaek, Sung Hyunoh, Choi Joong-On, Lee Junseop, Kim Sujin, Hwang Seong Jae, Rha Dong-Wook
Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Department of Artificial Intelligence, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Sci Rep. 2025 Apr 15;15(1):13039. doi: 10.1038/s41598-025-96949-w.
Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fracture risk, remains poorly understood in children. Herein, we developed/validated a deep learning-based model to predict pediatric BMD using plain spine radiographs. Using a two-stage model, Yolov8 was applied for vertebral body detection to predict BMD values using a regression model based on ResNet-18, from which a low-BMD group was classified based on Z-scores of predicted BMD. Patients aged 10-20-years who underwent dual-energy X-ray absorptiometry and radiography within 6 months at our hospital were enrolled. Ultimately, 601 patients (mean age, 14 years 4 months [SD 2 years]; 276 males) were included. The model achieved robust performance in detecting vertebral bodies (average precision [AP] 50 = 0.97, AP [50:95] = 0.68) and predicting BMD, with significant correlation (r = 0.72), showing consistency across different vertebral segments and agreement (intraclass correlation coefficient: 0.64). Moreover, it successfully classified low-BMD groups (area under the receiver operating characteristic curve = 0.85) with high sensitivity (0.76) and specificity (0.87). This deep-learning approach shows promise for BMD prediction and classification, with potential to enhance early detection and streamline bone health management in high-risk pediatric populations.
骨质疏松症是一种以骨矿物质密度（BMD）降低为特征的骨病，会导致机械强度下降和骨折风险增加，在儿童中仍未得到充分了解。在此，我们开发并验证了一种基于深度学习的模型，用于使用脊柱平片预测儿童的骨矿物质密度。采用两阶段模型，应用Yolov8进行椎体检测，然后使用基于ResNet-18的回归模型预测骨矿物质密度值，并根据预测的骨矿物质密度Z分数对低骨矿物质密度组进行分类。纳入了在我院6个月内接受双能X线吸收法和X线摄影的10至20岁患者。最终，纳入了601例患者（平均年龄14岁4个月[标准差2岁]；276例男性）。该模型在椎体检测（平均精度[AP]50 = 0.97，AP[50:95] = 0.68）和骨矿物质密度预测方面表现出色，具有显著相关性（r = 0.72），在不同椎体节段表现一致且具有一致性（组内相关系数：0.64）。此外，它成功地对低骨矿物质密度组进行了分类（受试者操作特征曲线下面积 = 0.85），具有高敏感性（0.76）和特异性（0.87）。这种深度学习方法在骨矿物质密度预测和分类方面显示出前景，有可能加强高危儿科人群的早期检测并简化骨骼健康管理。
