Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
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DOI：
10.1007/s00330-019-06167-y
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发表时间：
2019-10-01
影响因子：
5.9
通讯作者：
Liao, Chien-Hung
中科院分区：
文献类型：
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作者：
Cheng, Chi-Tung;Ho, Tsung-Ying;Liao, Chien-Hung
目的探讨深度卷积神经网络（DCNN）在骨盆X线平片（PXR）上检测和定位髋部骨折的可行性。背景资料概要髋部骨折是世界范围内老年人的主要健康问题。髋部骨折在X线检查中漏诊会导致预后不良。将DCNN应用于PXR可以潜在地提高髋部骨折诊断的准确性和效率。方法使用2012年1月至2017年12月期间的25，505张肢体X线照片对DCNN进行预训练。它在2008年8月至2016年12月期间使用3605个PXR进行了再训练。在2017年期间获得的100个独立PXR上评价了准确性、灵敏度、假阴性率和受试者工作特征曲线下面积（AUC）。作者还使用了可视化算法梯度加权类激活映射（Grad-CAM），以确认模型的有效性。结果该算法识别髋部骨折的准确率为91%，敏感性为98%，假阴性率为2%，AUC为0.98。可视化算法显示病变识别的准确率为95.9%。结论DCNN不仅在PXR上检出髋部骨折，假阴性率低，而且对骨折病灶定位准确性高。DCNN可能是一种有效且经济的模型，可以帮助临床医生在不中断当前临床路径的情况下进行诊断。
Objective To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Summary of background data Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis. Methods A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model. Results The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification. Conclusions A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway.