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.

报道了一种使用卷积网络来量化心脏双心室功能（体积，质量和射血分数）的新方法，并且在临床环境中验证了该方法的稳健性和计算时间。该方法使用解剖学信息来减少分类错误，并且在约 5 秒钟的时间内以与专家相当的准确度量化双心室功能和体积。