Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard
Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard
复制标题
前交叉韧带病变检测的深度学习方法：以关节镜作为参考标准评估诊断性能
DOI：
10.1002/jmri.27266
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发表时间：
2020-07-26
影响因子：
4.4
通讯作者：
Zhou, Quan
中科院分区：
文献类型：
--
作者：
Zhang, Lingyan;Li, Mifang;Zhou, Quan
背景MRI是诊断前交叉韧带损伤最常用的影像检查方法。然而，膝关节MRI的解读是时间密集型的，取决于读者的临床经验。基于深度学习算法的自动检测系统可以提高解释时间和可靠性。目的探讨利用深度学习方法在MRI上检测膝关节内前交叉韧带损伤的可行性。研究类型回顾。总共有163名前交叉韧带撕裂的受试者和245名前交叉韧带完好无损的受试者。分别有285卷、81卷和42卷用于训练、验证和测试集。1.5T和3.0T场强/序列二维矢状质子密度加权谱衰减反演恢复序列评估基于3D DenseNet的结构，我们构造了一个分类卷积神经网络。我们用不同的输入和另外两种算法(包括VGG16和ResNet)测试了这种深度学习方法。然后，我们让经验不足的放射科医生和资深放射科医生阅读核磁共振图像。统计学检验以关节镜检查结果为参考标准，对三种不同输入和三种不同算法的表现，住院医师和资深放射科医师评估其分类准确性、敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)和受试者工作特征曲线下面积(AUC)。结果自制3D深度学习结构的准确度、敏感度、特异度、预测值和净现值分别为0.957、0.976、0.944、0.940和0.976。ResNet、VGG16和我们建议的网络的平均AUC分别为0.946、0.859和0.960。我们的模型、住院医师和高级放射科医生的诊断准确率分别为0.957、0.814和0.899。数据结论本研究证明了使用基于深度学习的自动化检测系统来评估前交叉韧带损伤的可行性。证据级别3技术功效阶段1
Background MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection system based on a deep-learning algorithm may improve interpretation time and reliability. Purpose To determine the feasibility of using a deep learning approach to detect ACL injuries within the knee joint on MRI. Study Type Retrospective. Population In all, 163 subjects with an ACL tear and 245 subjects with an intact ACL. There were 285, 81, and 42 volumes for training, validation, and test sets, respectively. Field Strength/Sequence 2D sagittal proton density-weighted spectral attenuated inversion recovery sequences at 1.5T and 3.0T. Assessment Based on the architecture of 3D DenseNet, we constructed a classification convolutional neural network. We tested this deep learning approach with different inputs and two other algorithms, including VGG16 and ResNet. Then we had both inexperienced radiologists and senior radiologists read the MR images. Statistical Tests Using arthroscopic results as the reference standard, the performance of three different inputs and three different algorithms, the residents and senior radiologists assessed the classification accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results The accuracy, sensitivity, specificity, PPV, and NPV of our customized 3D deep learning architecture was 0.957, 0.976, 0.944, 0.940, and 0.976, respectively. The average AUCs were 0.946, 0.859, 0.960 for ResNet, VGG16, and our proposed network, respectively. The diagnostic accuracy of our model, residents, and senior radiologists was 0.957, 0.814, and 0.899, respectively. Data Conclusion Our study demonstrated the feasibility of using an automated deep-learning-based detection system to evaluate ACL injury. Level of Evidence 3 Technical Efficacy Stage 1