Liu Fang, Guan Bochen, Zhou Zhaoye, Samsonov Alexey, Rosas Humberto, Lian Kevin, Sharma Ruchi, Kanarek Andrew, Kim John, Guermazi Ali, Kijowski Richard
Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53705 (F.L., B.G., A.S., H.R., K.L., R.S., A.K., J.K., R.K.); Department of Electrical and Computer Engineering, University of Wisconsin School of Engineering, Madison, Wis (B.G.); Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minn (Z.Z.); and Department of Radiology, Boston University School of Medicine, Boston, Mass (A.G.).
Radiol Artif Intell. 2019 May 8;1(3):180091. doi: 10.1148/ryai.2019180091.
To investigate the feasibility of using a deep learning-based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard.
A fully automated deep learning-based diagnosis system was developed by using two deep convolutional neural networks (CNNs) to isolate the ACL on MR images followed by a classification CNN to detect structural abnormalities within the isolated ligament. With institutional review board approval, sagittal proton density-weighted and fat-suppressed T2-weighted fast spin-echo MR images of the knee in 175 subjects with a full-thickness ACL tear (98 male subjects and 77 female subjects; average age, 27.5 years) and 175 subjects with an intact ACL (100 male subjects and 75 female subjects; average age, 39.4 years) were retrospectively analyzed by using the deep learning approach. Sensitivity and specificity of the ACL tear detection system and five clinical radiologists for detecting an ACL tear were determined by using arthroscopic results as the reference standard. Receiver operating characteristic (ROC) analysis and two-sided exact binomial tests were used to further assess diagnostic performance.
The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at < .05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy.
There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.© RSNA, 2019
以关节镜检查为参考标准，探讨使用基于深度学习的方法在膝关节磁共振成像（MRI）中检测前交叉韧带（ACL）撕裂的可行性。
通过使用两个深度卷积神经网络（CNN）在MR图像上分离出ACL，随后使用一个分类CNN检测分离韧带内的结构异常，开发了一种基于深度学习的全自动诊断系统。经机构审查委员会批准，对175例ACL全层撕裂患者（98例男性，77例女性；平均年龄27.5岁）和175例ACL完整患者（100例男性，75例女性；平均年龄39.4岁）的膝关节矢状位质子密度加权和脂肪抑制T2加权快速自旋回波MR图像进行回顾性深度学习分析。以关节镜检查结果为参考标准，确定ACL撕裂检测系统和五位临床放射科医生检测ACL撕裂的敏感性和特异性。采用受试者操作特征（ROC）分析和双侧精确二项式检验进一步评估诊断性能。
ACL撕裂检测系统在最佳阈值时的敏感性和特异性分别为0.96和0.96。相比之下，临床放射科医生的敏感性在0.96至0.98之间，而特异性在0.90至0.98之间。在α <.05时，ACL撕裂检测系统与临床放射科医生的诊断性能无统计学显著差异。ACL撕裂检测系统的ROC曲线下面积为0.98，表明总体诊断准确性较高。
在MRI上确定是否存在ACL撕裂时，ACL撕裂检测系统与临床放射科医生的诊断性能无显著差异。©RSNA，2019
