Background and Objective

Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

Materials and Methods

A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features.

Results

AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC_AI = 0.870, AUC_CCTA = 0.781, P < 0.001). In identifying ≥ 50% stenotic vessels, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of CCTA-AI and traditional method are 86% and 83%, 88% and 59%, 85% and 94%, 73% and 84%, 94% and 83%, respectively. In the aspect of identifying plaque classification, accuracy of CCTA-AI is moderate compared to traditional CCTA (AUC = 0.750, P < 0.001).

Conclusion

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.

中文翻译：

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冠状动脉计算机断层血管造影成像中的深度学习分析，用于评估冠状动脉狭窄患者。

背景与目的

最近，深度卷积神经网络已大大改善了图像分类和图像分割。如果可以通过机器学习和深度学习来诊断冠状动脉疾病（CAD），它将大大减轻医生的负担，并加快关键患者的诊断速度。这项研究的目的是评估在冠状动脉狭窄中利用深度学习方法进行冠状动脉计算机断层血管造影（CCTA）成像（称为CCTA人工智能，CCTA-AI）的实用性。

材料和方法

通过利用深度学习和传递学习方法构建CCTA重建管道，以基于一系列二维（2D）CT图像生成自动重建的CCTA图像。回顾性分析了2017年6月至2017年12月连续接受CCTA和数字减影血管造影（DSA）的150例患者。数据集分为训练数据集和测试数据集两部分。训练数据集包括使用卷积神经网络（CNN）训练的100位患者的CCTA图像，以进一步识别各种斑块分类和冠状动脉狭窄。其余50名CAD患者作为测试数据集，通过将自动重构的CCTA图像与传统CCTA图像进行比较来评估，以DSA图像为参考方法。

结果

与传统方法相比，人工智能显着减少了后处理和诊断时间。在鉴别各种程度的冠状动脉狭窄时，CCTA-AI的诊断准确性优于传统CCTA（AUC _AI  = 0.870，AUC _CCTA = 0.781，P <0.001）。在识别≥50％的狭窄血管中，CCTA-AI和传统方法的准确性，敏感性，特异性，阳性预测值和阴性预测值分别为86％和83％，88％和59％，85％和94％，73％和84％，94％和83％。在斑块识别方面，与传统的CCTA相比，CCTA-AI的准确性中等（AUC = 0.750，P <0.001）。

结论

提出的CCTA-AI允许从一系列2D CT图像中生成自动重建的CCTA图像。与传统的CCTA相比，该方法对于检测≥50％的狭窄和分析斑块特征相对准确。