Mian Huang*
The University of Hong Kong, Hong Kong
Corresponding Author:
Mian Huang; The University of Hong Kong, Hong Kong
Keywords
AOSpine Classification; Thoracolumbar Fracture; Computed Tomography; Deep Learning; Osteoporotic Fracture; Bone Void
Abstract
Background: Accurate thoracolumbar fracture classification is central to treatment planning, but manual interpretation of CT images can be time-consuming and variable.
Objective: To develop a CT-based deep learning workflow for automated vertebral localization, fracture screening, AOSpine thoracolumbar ABC classification, and Osteoporotic Fracture (OF) grading.
Methods: This retrospective study included 845 spinal CT examinations with expert consensus labels. Total Segmentator was used for vertebral segmentation and level identification. Three-dimensional vertebral CT patches were then processed by cascaded 3D ResNet-18 models. The AOSpine model fused CT image features with four automatically extracted bone-void features: total void volume, void-volume ratio, void count, and maximum void volume. The OF model used a hierarchical multi-head structure for OF1–OF5 grading. Performance was assessed using Dice, level-identification accuracy, accuracy, precision, recall, specificity, F1-score, AUC, average precision, and quadratic weighted kappa.
Results: Total Segmentator achieved a mean Dice coefficient of 0.846 and vertebral level-identification accuracy of 92.22%. The fracture screening model achieved an accuracy of 0.988. For AOSpine ABC classification, the CT plus bone-void model achieved an overall accuracy of 62.5% and macro-F1 of 0.512. For OF grading, the hierarchical CT-only model achieved an accuracy of 0.737, macro-F1 of 0.692, quadratic weighted kappa of 0.639, macro-AUC of 0.894, and macro-AP of 0.767.
Conclusion: The proposed CT-based workflow demonstrated feasibility for automated vertebral segmentation, fracture screening, AOSpine ABC classification, and OF grading. Further multicenter validation is required before clinical deployment.
