Comparison of Artificial Intelligence Models Using CT Radiomics for Predicting Post-Vertebral Augmentation Residual Back Pain in Osteoporotic Vertebral Compression Fractures.

ABSTRACT: Background: Residual back pain (RBP) following vertebral augmentation (VA) represents a significant challenge in managing osteoporotic vertebral compression fractures (OVCFs). While conventional predictive models have shown moderate accuracy, their preoperative risk stratification capabilities remain suboptimal. CT-based radiomics has demonstrated success in vertebral fracture assessment, yet its integration with artificial intelligence (AI) for predicting RBP remains unexplored. Objective: This study aims to identify the optimal AI model for predicting RBP by systematically comparing multiple algorithms that integrate CT radiomics features with clinical parameters, with the goal of enabling preoperative risk stratification for improved surgical decision-making. Methods

SUBMITTER: Ge C 

PROVIDER: S-EPMC12320780 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature