基于超声心动图M(otion)模式的射血分数预测
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
AI总结：
本研究提出利用超声心动图的M模式生成多张人工图像并结合现成模型架构，辅以对比学习，在有限标注下高效准确地估计左心室射血分数并分类心肌病。
摘要：
通过常规筛查早期发现心功能不全对于诊断心血管疾病至关重要。左心室射血分数（EF）是心功能的重要指标，较低的EF与心肌病相关。超声心动图是心脏病学中常用的诊断工具，超声是一种低成本、实时且非电离的技术。然而，人工评估超声心动图以计算EF既耗时又需要专业知识，因此需要一种自动化方法。在本研究中，我们提出使用超声心动图的M(otion)模式来估计EF并对心肌病进行分类。我们从单张超声心动图生成多张人工M模式图像，并使用现成的模型架构将它们组合起来。此外，我们将对比学习（CL）扩展到心脏成像，通过利用未标记数据中的结构来学习有意义的表示，使模型即使在标注有限的情况下也能达到高准确率。我们的实验表明，监督设置仅需十种模式即可收敛，并且与基线方法相当，同时绕过了其繁琐的训练过程，在计算上更加高效。此外，使用M模式图像的CL对数据有限的场景（例如只有200名患者有标签）很有帮助，这在医学应用中很常见。
英文摘要：
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ejection fraction (EF), where lower EF is associated with cardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology, with ultrasound being a low-cost, real-time, and non-ionizing technology. However, human assessment of echocardiograms for calculating EF is time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we propose using the M(otion)-mode of echocardiograms for estimating the EF and classifying cardiomyopathy. We generate multiple artificial M-mode images from a single echocardiogram and combine them using off-the-shelf model architectures. Additionally, we extend contrastive learning (CL) to cardiac imaging to learn meaningful representations from exploiting structures in unlabeled data allowing the model to achieve high accuracy, even with limited annotations. Our experiments show that the supervised setting converges with only ten modes and is comparable to the baseline method while bypassing its cumbersome training process and being computationally much more efficient. Furthermore, CL using M-mode images is helpful for limited data scenarios, such as having labels for only 200 patients, which is common in medical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
