Spinal x-ray based scoliosis diagnosis using deep learning: a comparison of YOLOv11 and ResNet.

OBJECTIVES: To develop and evaluate an automated deep learning framework for scoliosis detection from spinal radiographs and to compare the performance of a single-stage YOLOv11-based approach with a two-stage ROI-guided ResNet model. METHODS: A publicly available spinal X-ray dataset was analyzed for key radiographic features, including spinal alignment, lateral curvature, vertebral asymmetry, and overall spinal contour. Two automated strategies were compared: a YOLOv11-based framework for spinal region detection and direct classification, and a two-stage approach in which the spinal region of interest was first localized using bounding box regression and then classified with ResNet. RESULTS: The YOLOv11-based framework outperformed the ROI-based ResNet approach in distinguishing scoliosis from non-scoliosis radiographs. It captured scoliosis-related deformity patterns more effectively and demonstrated stronger diagnostic stability across representative cases. CONCLUSIONS: The YOLOv11-based framework demonstrated superior performance over the two-stage ResNet strategy for automated scoliosis recognition and may serve as a useful adjunct for radiographic screening.