VerteRo: A Fully Automated Deep Neural Network Tool for Estimating Axial Vertebral Body Rotation in Adolescent Idiopathic Scoliosis.

Study DesignRetrospective validation study.ObjectivesTo develop and validate VerteRo, a fully automated deep learning-based tool that estimates axial vertebral body rotation (VBR) from standard posteroanterior (PA) scoliosis radiographs with high accuracy, overcoming the limitations of categorical grading systems.MethodsA multi-stage convolutional neural network pipeline incorporating automated region-of-interest (ROI) extraction, vertebral segmentation, pedicle localization, and geometric rotation estimation was trained on 97 AIS radiographs. Three object detection architectures (Faster R-CNN, YOLOv11 L, YOLOv12 L) were evaluated. The mean absolute error (MAE) relative to Stokes-derived reference angles was measured as the primary outcome and concordance with Nash-Moe grading was measured as the secondary outcome.ResultsFaster R-CNN demonstrated superior pedicle detection (F1 = 0.93) compared with YOLOv11 L and YOLOv12 L. VerteRo achieved a mean absolute error of 8.239° relative to reference standards. 71.34% of vertebrae demonstrated exact grade agreement with the Nash-Moe grading system.ConclusionsThis pilot study provides a proof-of-concept for a fully automated, end-to-end solution for quantitative axial rotation assessment from PA radiographs, offering improved objectivity over categorical grading methods. Its ability to generate quantitative axial rotation measures rather than coarse categorical grades has strong potential for supporting research applications and potential decision-support in clinical assessment following further validation.