论文标题
使用人工智能技术检测Cine磁共振图像中的纤维化图像
Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques
论文作者
论文摘要
背景：人工智能技术在心脏病学上表现出了巨大的潜力，尤其是检测人眼的不可感知的模式。从这个意义上讲，这些技术似乎足以鉴定心肌纹理中的模式，这可能导致表征和量化纤维化。目的：这项研究的目的是假设一种新的人工智能方法来鉴定Cine心脏磁共振（CMR）成像中的纤维化。方法：一项回顾性观察性研究是在圣卡洛斯·德·巴里洛切（San Carlos de Bariloche）临床中心的75名受试者中进行的。提出的方法使用卷积神经网络分析了Cine CMR图像中的心肌纹理，以确定局部心肌组织损伤。结果：量化验证数据集的量化局部组织损伤的精度为89％，测试集的精度为70％。此外，定性分析显示病变位置的空间相关性很高。结论：假定方法使仅使用Cine核磁共振研究中的信息在空间上识别纤维化，从而证明了该技术在将来量化心肌生存能力或研究病变病因的潜力
Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the lesions etiology
