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Background: Artificial intelligence techniques have shown great potential incardiology, especially in quantifying cardiac biventricular function, volume,mass, and ejection fraction (EF). However, its use in clinical practice is notstraightforward due to its poor reproducibility with cases from daily practice,among other reasons. Objectives: To validate a new artificial intelligence toolin order to quantify the cardiac biventricular function (volume, mass, and EF).To analyze its robustness in the clinical area, and the computational timescompared with conventional methods. Methods: A total of 189 patients wereanalyzed: 89 from a regional center and 100 from a public center. The methodproposes two convolutional networks that include anatomical information of theheart to reduce classification errors. Results: A high concordance (Pearsoncoefficient) was observed between manual quantification and the proposedquantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes andbiventricular EF) in about 5 seconds per study. Conclusions: This methodquantifies biventricular function and volumes in seconds with an accuracyequivalent to that of a specialist.

Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach