A prediction model of pediatric bone density from plain spine radiographs using deep learning.

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

SUBMITTER: Hong J 

PROVIDER: S-EPMC12000435 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature