Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.
Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.
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DOI：
10.1148/radiol.2018172986
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发表时间：
2018-10
期刊：
影响因子：
19.7
通讯作者：
Kijowski R
中科院分区：
文献类型：
--
作者：
Liu F;Zhou Z;Samsonov A;Blankenbaker D;Larison W;Kanarek A;Lian K;Kambhampati S;Kijowski R
目的：探讨使用深度学习方法检测膝关节内软骨损伤(包括软骨软化、纤颤、断裂、局灶性缺陷、软骨变性引起的弥漫性变薄和急性软骨损伤)的可行性。利用分割和分类卷积神经网络(CNN)开发了一种基于深度学习的全自动软骨损伤检测系统。采用深度学习的方法，对175例膝关节疼痛患者的膝关节脂肪抑制T2加权快速自旋回波序列MRI数据集进行了回顾性分析。训练CNN分类的参考标准是由接受过团契训练的肌肉骨骼放射科医生提供的关于放置在股骨和胫骨关节表面的17 395个小图像块中是否存在软骨病变的解释。使用受试者操作曲线分析和κ统计量来评估软骨损伤检测系统进行的两项单独评估的诊断性能和观察者内部对软骨损伤检测的一致性。以Youden指数为最佳阈值的软骨病变检测系统对评价1的敏感性和特异性分别为84.1%和85.2%，对评价2的敏感性和特异性分别为80.5%和87.9%。评价1和2的ROC曲线下面积分别为0.917和0.914，显示出较高的总体诊断准确性。两项单独评估之间的观察者内部一致性良好，κ为0.76。这项研究论证了使用基于深度学习的全自动软骨损伤检测系统来评估膝关节关节软骨损伤的可行性，该系统在检测软骨退变和急性软骨损伤方面具有较高的诊断性能和良好的观察者内一致性。该软骨病变检测系统(包括使用单个矢状位脂肪抑制T2加权快速自旋回波MRI序列)被发现具有与临床放射科医生在检测膝关节内软骨变性和急性软骨损伤方面具有不同经验的临床放射科医生的观察者间一致性，并且具有类似的诊断性能和更高的观察者间一致性。
To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. A fully automated deep learning–based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. This study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. This cartilage lesion detection system (involving the use of a single sagittal fat-suppressed T2-weighted fast spin-echo MRI sequence) was found to have similar diagnostic performance to and higher intraobserver agreement than the interobserver agreement of clinical radiologists with varying levels of experience for detecting cartilage degeneration and acute cartilage injury within the knee joint.
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