[论文解读] A Computer-Aided Diagnosis System Using Artificial Intelligence for Proximal Femoral Fractures Enables Residents to Achieve a Diagnostic Rate Equivalent to Orthopedic Surgeons - multi-institutional joint development research.

Yoichi Sato, Takamune Asamoto+4 more|arXiv (Cornell University)|Mar 11, 2020

Hip and Femur Fractures被引用 2

一句话总结

本研究基于多中心前后位髋关节X光片，开发了一种基于深度学习的计算机辅助诊断（CAD）系统，用于检测近端股骨骨折。该系统在10,484张图像（5,242张骨折和5,242张非骨折）上进行训练，取得了96.1%的准确率、95.2%的敏感度、96.9%的特异度以及0.99的AUC，Grad-CAM提供了可解释的诊断推理，与放射科医生的诊断决策一致。

ABSTRACT

[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 this http URL 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.

研究动机与目标

• 开发一种基于人工智能的计算机辅助诊断（CAD）系统，用于在普通前后位骨盆X光片上检测近端股骨骨折。
• 通过利用在大规模多机构数据集上训练的深度学习模型，提升住院医师的诊断准确性。
• 通过使用Grad-CAM可视化模型的诊断关注区域，实现实现透明的决策过程。
• 使用标准指标评估系统性能，并与经验丰富的骨科外科医生的诊断结果进行对比。
• 评估该CAD系统是否能在真实临床环境中实现与经验丰富的骨科外科医生相当的诊断性能。

提出的方法

• 使用PyTorch 1.3和预训练于ImageNet的EfficientNet-B4，训练了一个深度卷积神经网络，用于特征提取和分类。
• 数据集包含来自多个机构的4,851名患者的5,242张骨折侧和5,242张非骨折侧X光片，排除了影像不完整或特定骨折类型的病例。
• 模型评估采用标准指标：准确率、敏感度、特异度、F值以及受试者工作特征曲线下面积（AUC）。
• 应用梯度加权类激活映射（Grad-CAM）以可视化影响模型预测的X光片中的感兴趣区域。
• 在包含10,484张图像的平衡数据集上端到端训练模型，以确保在各种骨折类型上的鲁棒泛化能力。
• 数据预处理包括标准化和增强，以提高模型泛化能力并减少过拟合。

实验结果

研究问题

• RQ1 基于深度学习的CAD系统是否能在普通X光片上检测近端股骨骨折时，实现与经验丰富的骨科外科医生相当的诊断准确率？
• RQ2 该模型在大规模多中心髋关节X光片数据集上的敏感度、特异度和AUC表现如何？
• RQ3 Grad-CAM在多大程度上能够提供可解释且具有临床相关性的模型诊断推理可视化？
• RQ4 该CAD系统是否能支持经验较少的临床医生（如住院医师）实现与专家相当的诊断性能？
• RQ5 该模型是否能通过基于注意力的可视化技术可靠地识别X光片中的骨折相关解剖区域？

主要发现

• 该CAD系统在10,484张前后位髋关节X光片的测试集中实现了96.1%的诊断准确率。
• 该模型表现出较高的敏感度（95.2%）和特异度（96.9%），表明其在检测真实骨折和正确识别正常病例方面均表现优异。
• 受试者工作特征曲线下面积（AUC）达到0.99，表明其在区分骨折与非骨折病例方面具有极佳的判别能力。
• F值为0.961，反映了精确率与召回率的平衡调和均值。
• Grad-CAM可视化结果正确突出了与近端股骨骨折相关的解剖区域，证实了模型的诊断依据与临床推理一致。
• 该系统的性能表明，使用此CAD工具的住院医师可实现与经验丰富的骨科外科医生相当的诊断准确率。