Cervical spine fractures constitute a critical medical emergency, with the potential for lifelong paralysis or even fatality if left untreated or undetected. Over time, these fractures can deteriorate without intervention. To address the lack of research on the practical application of deep learning techniques for the detection of spine fractures, this study leverages a dataset containing both cervical spine fractures and non-fractured computed tomography images. This paper introduces a two-stage pipeline designed to identify the presence of cervical vertebrae in each image slice and pinpoint the location of fractures. In the first stage, a multi-input network, incorporating image and image metadata, is trained. This network is based on the Global Context Vision Transformer, and its performance is benchmarked against popular deep learning image classification model. In the second stage, a YOLOv8 model is trained to detect fractures within the images, and its effectiveness is compared to YOLOv5. The obtained results indicate that the proposed algorithm significantly reduces the workload of radiologists and enhances the accuracy of fracture detection.

翻译：颈椎骨折是一种严重的医疗急症，若未能及时发现或治疗，可能导致终身瘫痪甚至死亡。随着时间的推移，未经干预的骨折可能进一步恶化。针对深度学习技术在脊柱骨折实际检测应用中研究不足的问题，本研究利用包含颈椎骨折与非骨折计算机断层扫描图像的公开数据集，提出了一种两阶段检测流程，旨在识别每个图像切片中是否存在颈椎，并准确定位骨折位置。第一阶段训练了一个融合图像及其元数据的多输入网络，该网络基于全局上下文视觉Transformer，并与主流深度学习图像分类模型的性能进行对比；第二阶段训练了YOLOv8模型用于检测图像中的骨折，并与YOLOv5进行效果比较。实验结果表明，该算法显著减轻了放射科医师的工作负担，并提高了骨折检测的准确性。