论文标题
自动量化体积和心脏共振中的双心脑功能。新的人工智能方法的验证
Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach
论文作者
论文摘要
背景：人工智能技术在心脏病学方面表现出巨大的潜力，尤其是在量化心脏双脑室功能，体积，质量和射血分数（EF）方面。但是，由于其对日常实践的病例的可重复性差，因此在临床实践中的使用并不是一件直接的。目的：验证一种新的人工智能工具，以量化心脏双心室功能（体积，质量和EF）。分析其在临床领域的鲁棒性以及与常规方法相比的计算时间。方法：总共分析了189名患者：来自区域中心的89例，100例公共中心。该方法提出了两个卷积网络，其中包括心脏的解剖信息，以减少分类错误。结果：在大约5秒钟内，在手动定量和心脏功能的拟议定量和双脑动脉表EF的拟议定量之间观察到了高的一致性（Pearson系数）。结论：此方法以秒的精度量化了双室功能和体积，相当于专家的精度。
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
