论文标题
使用人工智能用于髋部骨折的计算机辅助诊断系统 - 彻底的联合发展研究 -
A Computer-Aided Diagnosis System Using Artificial Intelligence for Hip Fractures -Multi-Institutional Joint Development Research-
论文作者
论文摘要
[目的]开发一个计算机辅助诊断系统（CAD）系统，用于平面额头X射线射线，并在多个中心收集的大型数据集中进行了深度学习模型。 [材料和方法]。我们包括了5295例颈部骨折或动骨骨折病例，这些病例被骨科医生诊断和治疗，使用X射线X射线或计算机层析成像（CT）或磁共振成像（MRI）（MRI）在2009年4月至2019年3月之间访问每个机构。排除在照片范围内不包括两个臀部的病例，股骨轴骨折和周围骨折的病例被排除在外，并且使用了从4,851例中获得的5242个平面额骨X射线进行机器学习。这些图像被分为5242张图像，包括断裂侧和5242张图像，而没有断裂侧，总共使用了10484张图像进行机器学习。使用深层卷积神经网络方法进行机器学习。使用Pytorch 1.3和fast.ai 1.0用作框架，并且使用了预训练的成像网模型的EfficityNet-B4。在最终评估中，评估了曲线（AUC）下的准确性，灵敏度，特异性，F值和面积。梯度加权类激活映射（GRAD-CAM）用于概念化CAD系统的诊断基础。 [结果]学习模型的诊断准确性为96。1％，灵敏度为95.2％，特异性为96.9％，F值为0.961，AUC为0.99。诊断正确的病例通常使用Grad-CAM显示出正确的诊断基础。 [结论]我们开发的使用深度学习模型的CAD系统能够以高精度诊断X射线中的髋部骨折，并且有可能提出决策原因。
[Objective] To develop a Computer-aided diagnosis (CAD) system for plane frontal hip X-rays with a deep learning model trained on a large dataset collected at multiple centers. [Materials and Methods]. We included 5295 cases with neck fracture or trochanteric fracture who were diagnosed and treated by orthopedic surgeons using plane X-rays or computed tomography (CT) or magnetic resonance imaging (MRI) who visited each institution between April 2009 and March 2019 were enrolled. Cases in which both hips were not included in the photographing range, femoral shaft fractures, and periprosthetic fractures were excluded, and 5242 plane frontal pelvic X-rays obtained from 4,851 cases were used for machine learning. These images were divided into 5242 images including the fracture side and 5242 images without the fracture side, and a total of 10484 images were used for machine learning. A deep convolutional neural network approach was used for machine learning. Pytorch 1.3 and Fast.ai 1.0 were used as frameworks, and EfficientNet-B4, which is pre-trained ImageNet model, was used. In the final evaluation, accuracy, sensitivity, specificity, F-value and area under the curve (AUC) were evaluated. Gradient-weighted class activation mapping (Grad-CAM) was used to conceptualize the diagnostic basis of the CAD system. [Results] The diagnostic accuracy of the learning model was accuracy of 96. 1 %, sensitivity of 95.2 %, specificity of 96.9 %, F-value of 0.961, and AUC of 0.99. The cases who were correct for the diagnosis showed generally correct diagnostic basis using Grad-CAM. [Conclusions] The CAD system using deep learning model which we developed was able to diagnose hip fracture in the plane X-ray with the high accuracy, and it was possible to present the decision reason.
