Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.

对比增强心脏磁共振成像（MRI）通常用于确定心肌疤痕负担并做出冠状动脉血运重建的治疗决策。目前，还没有优化的深度学习算法来自动分类疤痕心肌与正常心肌。我们报告了一种改进的生成对抗网络（GAN）增强方法，以使用临床前和临床方法改进心肌疤痕的二元分类。对于 MobileNetV2 平台的初始训练，我们使用了急性心肌梗死 (MI) 小鼠模型的高场 (9.4T) 心脏 MRI 生成的图像。一旦系统显示小鼠急性心肌梗死分类的准确性为 100%，我们就在 91 名患有缺血性心肌疤痕的患者和 31 名没有心肌疤痕证据的对照受试者中测试了该方法的转化意义。为了获得可比较的增强数据集，我们将疤痕图像旋转 8 次，将对照图像旋转 72 次，总共生成 6,684 张疤痕图像和 7,451 张对照图像。在人类中，使用基于渐进式生长 GAN (PGGAN) 的增强显示出 93% 的分类准确率，远远优于传统的自动化模块。在我们的 CNN 中使用其他注意力模块进一步将分类准确率提高了 5%。这些数据具有很高的转化意义，未来需要进行更大规模的多中心研究来验证临床意义。