论文标题
从延迟增强心脏MRI和临床信息的自动心肌疾病预测
Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information
论文作者
论文摘要
延迟增强心脏磁共振（DE-CMR）提供了有关心肌生存能力的重要诊断和预后信息。 DE-CMR中Gadolinium后期增强（LGE）的存在和程度与血运重建后左心室功能的改善的可能性负相关。此外，LGE的发现可以支持对其他几种心肌病的诊断，但是它们的缺席并不排除它们，从而使视觉评估的疾病分类变得困难。在这项工作中，我们提出了深度学习神经网络，可以自动从患者临床信息和DE-CMR中预测心肌疾病。所有提出的网络都具有很好的分类精度（> 85％）。在此分类任务中包括DE-CMR的信息（DE-CMR分割后直接作为图像或作为元数据）很有价值，将准确性提高到95-100％。
Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important diagnostic and prognostic information on myocardial viability. The presence and extent of late gadolinium enhancement (LGE)in DE-CMR is negatively associated with the probability of improvement in left ventricular function after revascularization. Moreover, LGE findings can support the diagnosis of several other cardiomyopathies, but their absence does not rule them out, making disease classification by visual assessment difficult. In this work, we propose deep learning neural networks that can automatically predict myocardial disease from patient clinical information and DE-CMR. All the proposed networks achieve very good classification accuracy (>85%). Including information from DE-CMR (directly as images or as metadata following DE-CMR segmentation) is valuable in this classification task, improving the accuracy to 95-100%.
