Artificial intelligence to diagnose meniscus tears on MRI
Artificial intelligence to diagnose meniscus tears on MRI
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DOI：
10.1016/j.diii.2019.02.007
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发表时间：
2019-04-01
影响因子：
5.5
通讯作者：
Fournier, L.
中科院分区：
文献类型：
--
作者：
Roblot, V;Giret, Y.;Fournier, L.
目的：本研究的目的是建立和评估一种高性能的算法来检测和表征膝关节磁共振成像检查(MRI)中半月板撕裂的存在。材料和方法：基于1123张膝关节MR图像的数据集训练算法。我们将主任务分为三个子任务：首先检测两个角的位置，其次检测撕裂的存在，最后确定撕裂的方向。提出了一种基于快速区域卷积神经网络和快速区域卷积神经网络的任务分类算法。因此，该算法被用于由700幅图像组成的测试数据集进行外部验证。结果：使用我们的算法检测两个半月板角的位置的AUC为0.92，存在半月板撕裂的AUC为0.94，确定撕裂方向的AUC为083，最终加权AUC为0.90。结论：我们的基于快速区域CNN的算法能够检测半月板撕裂，是朝着开发更多端到端人工智能支持的诊断工具迈出的第一步。(C)2019年，埃尔塞维尔·马森公司代表法国兴业银行出版。
Purpose: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.Material and methods: An algorithm was trained on a dataset of 1123 MR images of the knee. We separated the main task into three sub-tasks: first to detect the position of both horns, second to detect the presence of a tear, and last to determine the orientation of the tear. An algorithm based on fast-region convolutional neural network (CNN) and faster-region CNN, was developed to classify the tasks. The algorithm was thus used on a test dataset composed of 700 images for external validation. The performance metric was based on area under the curve (AUC) analysis for each task and a final weighted AUC encompassing the three tasks was calculated.Results: The use of our algorithm yielded an AUC of 0.92 for the detection of the position of the two meniscal horns, of 0.94 for the presence of a meniscal tear and of 083 for determining the orientation of the tear, resulting in a final weighted AUC of 0.90.Conclusion: We demonstrate that our algorithm based on fast-region CNN is able to detect meniscal tears and is a first step towards developing more end-to-end artificial intelligence-powered diagnostic tools. (C) 2019 Published by Elsevier Masson SAS on behalf of Societe francaise de radiologie.