This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture detection on radiographs of various body parts. A total of 9040 radiographic studies were extracted. These images were classified into several body part types including 1651 hand, 1302 wrist, 406 elbow, 696 shoulder, 1580 pelvic, 948 knee, 1180 ankle, and 1277 foot images. Instance segmentation was annotated by radiologists. The ResNext-101+FPN was employed as the baseline network structure and the FAMO model for processing. The proposed FAMO model and other ablative models were tested on a test set of 20% total radiographs in a balanced body part distribution. To the per-fracture extent, an AP (average precision) analysis was performed. For per-image and per-case, the sensitivity, specificity, and AUC (area under the receiver operating characteristic curve) were analyzed. At the per-fracture level, the controlled experiment set the baseline AP to 76.8% (95% CI: 76.1%, 77.4%), and the major experiment using FAMO as a preprocessor improved the AP to 77.4% (95% CI: 76.6%, 78.2%). At the per-image level, the sensitivity, specificity, and AUC were 61.9% (95% CI: 58.7%, 65.0%), 91.5% (95% CI: 89.5%, 93.3%), and 74.9% (95% CI: 74.1%, 75.7%), respectively, for the controlled experiment, and 64.5% (95% CI: 61.3%, 67.5%), 92.9% (95% CI: 91.0%, 94.5%), and 77.5% (95% CI: 76.5%, 78.5%), respectively, for the experiment with FAMO. At the per-case level, the sensitivity, specificity, and AUC were 74.9% (95% CI: 70.6%, 78.7%), 91.7%% (95% CI: 88.8%, 93.9%), and 85.7% (95% CI: 84.8%, 86.5%), respectively, for the controlled experiment, and 77.5% (95% CI: 73.3%, 81.1%), 93.4% (95% CI: 90.7%, 95.4%), and 86.5% (95% CI: 85.6%, 87.4%), respectively, for the experiment with FAMO. In conclusion, in bone fracture detection, FAMO is an effective preprocessor to enhance model performance by mitigating feature ambiguity in the network.

本研究的目的是提出一种方法，即特征模糊性缓解算子 (FAMO) 模型，以减轻身体各部位 X 光照片骨折检测中的特征模糊性。总共提取了 9040 个射线照相研究。这些图像被分为多个身体部位类型，包括 1651 个手、1302 个手腕、406 个肘部、696 个肩部、1580 个骨盆、948 个膝盖、1180 个脚踝和 1277 个脚部图像。实例分割由放射科医生注释。采用ResNext-101+FPN作为基线网络结构和FAMO模型进行处理。所提出的 FAMO 模型和其他消融模型在平衡身体部位分布的 20% 总射线照片的测试集上进行了测试。对于每次骨折的程度，进行了 AP（平均精度）分析。对于每个图像和每个病例，分析了敏感性、特异性和 AUC（受试者工作特征曲线下面积）。在每次骨折水平上，对照实验将基线 AP 设置为 76.8%（95% CI：76.1%、77.4%），使用 FAMO 作为预处理器的主要实验将 AP 提高到 77.4%（95% CI：76.6） %，78.2%）。在每幅图像水平上，敏感性、特异性和 AUC 分别为 61.9% (95% CI: 58.7%, 65.0%)、91.5% (95% CI: 89.5%, 93.3%) 和 74.9% (95% CI: 89.5%, 93.3%) 和 74.9% (95% CI ：对照实验分别为 74.1%、75.7%）、64.5%（95% CI：61.3%、67.5%）、92.9%（95% CI：91.0%、94.5%）和 77.5%（95%对于 FAMO 实验，CI 分别为 76.5%、78.5%。在每个病例水平上，敏感性、特异性和 AUC 分别为 74.9% (95% CI: 70.6%, 78.7%)、91.7%% (95% CI: 88.8%, 93.9%) 和 85.7% (95% CI)对于对照实验，CI 分别为 84.8%、86.5%）、77.5%（95% CI：73.3%、81.1%）、93.4%（95% CI：90.7%、95.4%）和 86.5%（95 % CI：85.6%，87。4%），分别用于 FAMO 实验。总之，在骨折检测中，FAMO 是一种有效的预处理器，可以通过减轻网络中的特征模糊性来增强模型性能。