Marting Victoria, Borren Noor, van Diepen Max R, van Lieshout Esther M M, Wijffels Mathieu M E, van Walsum Theo
Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Int J Comput Assist Radiol Surg. 2025 May 16. doi: 10.1007/s11548-025-03390-5.
Trauma-induced rib fractures are a common injury. The number and characteristics of these fractures influence whether a patient is treated nonoperatively or surgically. Rib fractures are typically diagnosed using CT scans, yet 19.2-26.8% of fractures are still missed during assessment. Another challenge in managing rib fractures is the interobserver variability in their classification. Purpose of this study was to develop and assess an automated method that detects rib fractures in CT scans, and classifies them according to the Chest Wall Injury Society (CWIS) classification.
198 CT scans were collected, of which 170 were used for training and internal validation, and 28 for external validation. Fractures and their classifications were manually annotated in each of the scans. A detection and classification network was trained for each of the three components of the CWIS classifications. In addition, a rib number labeling network was trained for obtaining the rib number of a fracture. Experiments were performed to assess the method performance.
On the internal test set, the method achieved a detection sensitivity of 80%, at a precision of 87%, and an F1-score of 83%, with a mean number of FPPS (false positives per scan) of 1.11. Classification sensitivity varied, with the lowest being 25% for complex fractures and the highest being 97% for posterior fractures. The correct rib number was assigned to 94% of the detected fractures. The custom-trained nnU-Net correctly labeled 95.5% of all ribs and 98.4% of fractured ribs in 30 patients. The detection and classification performance on the external validation dataset was slightly better, with a fracture detection sensitivity of 84%, precision of 85%, F1-score of 84%, FPPS of 0.96 and 95% of the fractures were assigned the correct rib number.
The method developed is able to accurately detect and classify rib fractures in CT scans, there is room for improvement in the (rare and) underrepresented classes in the training set.
创伤性肋骨骨折是一种常见损伤。这些骨折的数量和特征会影响患者接受非手术治疗还是手术治疗。肋骨骨折通常通过CT扫描进行诊断，但在评估过程中仍有19.2%至26.8%的骨折被漏诊。管理肋骨骨折的另一个挑战是观察者之间对其分类的差异。本研究的目的是开发并评估一种能在CT扫描中检测肋骨骨折并根据胸壁损伤协会（CWIS）分类进行分类的自动化方法。
收集了198份CT扫描，其中170份用于训练和内部验证，28份用于外部验证。在每份扫描中手动标注骨折及其分类。针对CWIS分类的三个组成部分分别训练了一个检测和分类网络。此外，还训练了一个肋骨编号标注网络以获取骨折的肋骨编号。进行实验以评估该方法的性能。
在内部测试集上，该方法的检测灵敏度为80%，精度为87%，F1分数为83%，平均每扫描假阳性数（FPPS）为1.11。分类灵敏度各不相同，复杂骨折的最低灵敏度为25%，后部骨折的最高灵敏度为97%。94%的检测到的骨折被赋予了正确的肋骨编号。在30名患者中，定制训练的nnU-Net正确标注了所有肋骨的95.5%和骨折肋骨的98.4%。在外部验证数据集上的检测和分类性能略好，骨折检测灵敏度为84%，精度为85%，F1分数为84%，FPPS为0.96，95%的骨折被赋予了正确的肋骨编号。
所开发的方法能够在CT扫描中准确检测和分类肋骨骨折，训练集中（罕见且）代表性不足的类别仍有改进空间。
