基于 CNN1 和 CNN2 的磁共振图像融合诊断膝关节半月板损伤与 CT 的对比分析。
Fusion of CNN1 and CNN2-based magnetic resonance image diagnosis of knee meniscus injury and a comparative analysis with computed tomography.
机构信息
The first People's Hospital of Changzhou, China.
The first People's Hospital of Changzhou, China.
出版信息
Comput Methods Programs Biomed. 2021 Nov;211:106297. doi: 10.1016/j.cmpb.2021.106297. Epub 2021 Jul 22.
PURPOSE
We used convolutional neural network (CNN) technology to improve the accuracy of diagnosis of knee meniscus injury and shorten the diagnosis time.
METHOD
We propose a meniscus detection method based on Fusion of CNN1 and CNN2 (CNNf), which uses Magnetic Resonance Imaging (MRI) and Computer tomography (CT) to compare the diagnosis results, verifies the proposed method through 2460 images collected from 205 patients in the hospital. We used accuracy, sensitivity, specificity, receiver operating characteristics (ROC), and damage total rate to evaluate performance.
RESULTS
The accuracy of our model was 93.86%, the sensitivity was 91.35%, the specificity was 94.65%, and the area under the receiver operating characteristic curve was 96.78%. The total damage rate of MRI is 91.57%, which is far greater than the total damage rate of CT diagnosis of 80.13%.
CONCLUSION
CNNf-based MRI technology of knee meniscus injury has high practical value in clinical practice. It can effectively improve the accuracy of diagnosis and reduce the rate of misdiagnosis.
目的
我们使用卷积神经网络（CNN）技术来提高膝关节半月板损伤诊断的准确性并缩短诊断时间。
方法
我们提出了一种基于 CNN1 和 CNN2 融合的半月板检测方法（CNNf），该方法使用磁共振成像（MRI）和计算机断层扫描（CT）来比较诊断结果，通过从医院收集的 205 名患者的 2460 张图像对提出的方法进行了验证。我们使用准确性、敏感性、特异性、接收者操作特征（ROC）和损伤总率来评估性能。
结果
我们的模型的准确率为 93.86%，敏感性为 91.35%，特异性为 94.65%，ROC 曲线下面积为 96.78%。MRI 的总损伤率为 91.57%，远高于 CT 诊断的 80.13%总损伤率。
结论
基于 CNNf 的膝关节半月板损伤 MRI 技术在临床实践中具有很高的实用价值。它可以有效提高诊断的准确性，降低误诊率。
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