Deep Learning-Based Projection Angle Estimation for Lumbar Oblique Radiography: A Two-Stage Object Detection Approach Using Vertebral–Pedicle Ratio Analysis

Lumbar oblique radiography plays a crucial role in diagnosing spinal disorders, particularly spondylolysis and spondylolisthesis. Achieving optimal projection angles remains challenging due to variability in positioning techniques and subjective quality assessment. This study presents a deep learning framework for automatic angle estimation in lumbar oblique X-ray images using a two-stage object detection approach. Training data consisted of synthetic X-ray images generated from CT datasets with known projection angles (20° to 60°), annotated with three classes: L2–L4 vertebral levels, vertebral bodies, and pedicles. Two detection models were compared: Model1, a three-class whole-image detector, and Model2, a single-class pedicle detector applied to vertebral body crops from Model1. The Vertebral–Pedicle Ratio (VPR) was used to estimate projection angle via separate linear regression for negative-angle (n-group) and positive-angle (p-group) projections. Five-fold cross-validation showed Model2 achieved higher detection performance (macro mean [email protected] = 0.913, mean DSC = 0.825) than Model1 (macro mean [email protected] = 0.762, mean DSC = 0.791). Pooled regression yielded R2_n = 0.832 and R2_p = 0.870. Angle estimation with Model2 achieved MAE = 5.42° (SD 1.08°), substantially lower than Model1 (MAE = 9.57°, SD 1.64°), while Model1 offered faster throughput (18.3 FPS vs. 2.9 FPS). Two-stage pedicle detection using VPR-based linear regression provides clinically acceptable angle estimation accuracy in lumbar oblique radiography. Automated angle verification enables real-time positioning feedback during imaging, post-imaging image quality documentation in PACS, and retrospective auditing of facility positioning protocols. These comprehensive implementations are expected to standardize lumbar oblique radiography.