Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases treated annually in Germany. The AO/OTA system provides a structured fracture type classification, which serves as the foundation for treatment decisions. Although accurately classifying fractures can be challenging, current deep learning models have demonstrated performance comparable to that of experienced radiologists. While most existing approaches rely solely on radiographs, the potential impact of incorporating other additional modalities, such as automatic bone segmentation, fracture location, and radiology reports, remains underexplored. In this work, we systematically analyse the contribution of these three additional information types, finding that combining them with radiographs increases the AUROC from 91.71 to 93.25. Our code is available on GitHub.

翻译：骨折，特别是前臂远端骨折，是儿童和青少年最常见的损伤之一，在德国每年约有80万例接受治疗。AO/OTA系统提供了结构化的骨折类型分类，这是治疗决策的基础。尽管准确分类骨折可能具有挑战性，但当前的深度学习模型已展现出与经验丰富的放射科医生相当的性能。虽然大多数现有方法仅依赖X光片，但整合其他附加模态（如自动骨骼分割、骨折定位和放射学报告）的潜在影响仍未得到充分探索。在本研究中，我们系统分析了这三种附加信息类型的贡献，发现将它们与X光片结合可将AUROC从91.71提升至93.25。我们的代码已在GitHub上开源。