  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.

Image and Video Processing;Artificial Intelligence;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics