Purpose

To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists.

Materials and methods

We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample.

Results

The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test.

Conclusion

The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.

中文翻译：

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深度学习辅助识别急性缺血性卒中高密度 MCA 征象的有用性：与读者表现的比较。

目的

与神经放射科医生的诊断性能相比，评估深度学习辅助诊断在非对比计算机断层扫描上识别高密度大脑中动脉征 (HMCAS) 的有用性。

材料和方法

我们获得了使用 50 像素直径的圆形感兴趣区域提取的 46 个 HMCAS 阳性和 52 个 HMCAS 阴性测试样本。五位神经放射科医生通过描述每个样本中的 HMCAS 阳性预测率进行了初步诊断性能测试。他们的诊断性能与我们之前研究中使用另一个数据集训练的深度卷积神经网络 (DCNN) 模型进行了比较。在第二个测试中，读者可以参考每个样本中 DCNN 模型的预测率。

结果

DCNN 对 HMCAS 的诊断性能显示准确率为 81.6%，接受者操作特征曲线下面积 (AUC) 为 0.869，而神经放射科医生的初始诊断性能显示准确率为 78.8%，AUC 为 0.882。神经放射科医生参考 DCNN 模型的结果进行的第二次诊断测试显示准确率为 84.7%，AUC 为 0.932。在所有阅读器中，第二次测试的 AUC 值高于初始测试。

结论

DCNN 识别 HMCAS 的能力可与神经放射科医生的诊断性能相媲美。