A prediction model of pediatric bone density from plain spine radiographs using deep learning - Juntaek Hong; Hyunoh Sung; Joong-on Choi; Junseop Lee; Sujin Kim; Seong Jae Hwang; Dong-wook Rha (Scientific Reports)
Abstract 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.