Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection
Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection
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DOI：
10.1016/j.ejrad.2020.109139
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发表时间：
2020-09-01
影响因子：
3.3
通讯作者：
Kitamura, Gene
中科院分区：
文献类型：
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作者：
Kitamura, Gene
目的：最近的文献表明深度学习在骨盆X线片中检测髋部骨折的有效性，但利用深度学习来检测骨盆和髋臼骨折的研究还很少。创建深度学习模型还需要适当地标记X射线位置和硬件存在。我们的目的是训练和测试深度学习模型，以检测骨盆X线片位置、硬件存在、骨盆和髋臼骨折以及髋部骨折。材料和方法：数据收集于2009年8月至2019年6月。将数据子集分解为4个位置标签和2个硬件标签，以创建位置标注和硬件检测模型。剩余的数据用这些训练好的模型进行解析，这些模型基于6个单独的骨折模式进行标记，并创建了各种骨折检测模型。结果：位置模型和硬件模型均表现良好，AUC在0.99~1.00之间。检测股骨近端骨折的AUC高达0.95，与先前发表的研究一致。在分离骨折模型下，骨盆和髋臼骨折检测性能分别为0.70和0.85。结论：我们成功地建立了深度学习模型，该模型可以检测骨盆成像位置、硬件存在以及骨盆和髋臼骨折，股骨近端骨折的AUC损失仅为0.03。
Purpose: Recent papers have shown the utility of deep learning in detecting hip fractures with pelvic radiographs, but there is a paucity of research utilizing deep learning to detect pelvic and acetabular fractures. Creating deep learning models also requires appropriately labeling x-ray positions and hardware presence. Our purpose is to train and test deep learning models to detect pelvic radiograph position, hardware presence, and pelvic and acetabular fractures in addition to hip fractures.Material and methods: Data was retrospectively acquired between 8/2009-6/2019. A subset of the data was split into 4 position labels and 2 hardware labels to create position labeling and hardware detecting models. The remaining data was parsed with these trained models, labeled based on 6 "separate" fracture patterns, and various fracture detecting models were created. A receiver operator characteristic (ROC) curve, area under the curve (AUC), and other output metrics were evaluated.Results: The position and hardware models performed well with AUC of 0.99-1.00. The AUC for proximal femoral fracture detection was as high as 0.95, which was in line with previously published research. Pelvic and acetabular fracture detection performance was as low as 0.70 for the posterior pelvis category and as high as 0.85 for the acetabular category with the "separate" fracture model.Conclusion: We successfully created deep learning models that can detect pelvic imaging position, hardware presence, and pelvic and acetabular fractures with AUC loss of only 0.03 for proximal femoral fracture.