Vertebral fractures are prevalent skeletal injuries commonly associated with osteoporosis, trauma, and degenerative diseases. Early and accurate diagnosis is crucial to prevent complications such as chronic pain and progressive spinal deformities. In recent years, artificial intelligence (AI) has emerged as a powerful tool in medical imaging to support automatic detection and classification of vertebral fractures. This review provides an overview of AI-based approaches for spinal fracture diagnosis and summarizes recent advances in deep learning (DL) and machine learning (ML) models. The performance of AI models, mainly evaluated by sensitivity, specificity, and accuracy metrics, varies with imaging modality and dataset size, with computed tomography-based models demonstrating superior diagnostic accuracy. In addition, AI-assisted workflows have been shown to improve diagnostic efficiency, reducing the time required for fracture detection. Despite these advances, challenges remain, such as dataset variability, the need for large-scale annotated datasets, and standardization of evaluation metrics. Future research should focus on improving model generalization, integrating multimodal imaging data, and validating AI applications in real-world clinical settings to further improve vertebral fracture diagnosis and patient management.
Jeong et al. (Wed,) studied this question.