融合影像与辅助信息，提升儿童腕部骨折分类准确率
A Systematic Analysis of Input Modalities for Fracture Classification of the Paediatric Wrist
- 结合放射片与骨骼分割、骨折位置、报告文本三类额外信息
- 模型AUROC从91.71提升至93.25，性能显著增强
- 适合医疗AI研究者与放射科医生参考
儿童及青少年中，尤其是远端桡骨部位的骨折是最常见的损伤，德国每年约有80万例病例接受治疗。AO/OTA分类系统为骨折类型提供了结构化标准，是制定治疗方案的基础。尽管准确分类骨折具有挑战性，但当前深度学习模型的表现已可媲美经验丰富的放射科医生。然而，现有方法多仅依赖放射片，而自动骨骼分割、骨折位置信息以及放射科报告等附加模态的潜在价值尚未充分探索。本文系统分析了这三种附加信息对分类任务的贡献，发现将其与放射片结合后，模型的AUROC从91.71提升至93.25。相关代码已开源于GitHub。
原文摘要 · Abstract (English)
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.
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