Spinal x-ray based scoliosis diagnosis using deep learning: a comparison of YOLOv11 and ResNet.
Authors
Affiliations (2)
Affiliations (2)
- First Clinical Medical College, Anhui Medical University, Hefei, China.
- Department of Spine, Third Affiliated Hospital of Anhui Medical University, Hefei, China.
Abstract
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. 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. 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. 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.