Left ventricular ejection fraction (LVEF) is a commonly used index of cardiac functionality. Thus, accuracy in its measurement is fundamental. LVEF measure is usually manually performed by clinicians from echo-cardiographic images. Use of automatic algorithms could make LVEF measurement more objective. Thus, the aim of the present work is to pre-sent DL-LVEF, a new automatic algorithm for LVEF measurement. DL-LVEF was implemented in Google Colab Pro and includes two computa-tional phases, which are: 1) deep-learning identification and segmenta-tion of the left ventricular endocardium, performed by combining the YOLOv7 algorithm with a U-Net; and 2) LVEF computation, based on the Simpson's rule. DL-LVEF was set up and tested on the CAMUS database, which includes 1800 echocardiographic images acquired from 450 pa-tients with annotated LVEF values and manual segmentation of the left ventricular endocardium. The database was divided into training dataset (70%) and testing dataset (30%); 14% of the training dataset was used as validation dataset for defining the early stopping point. In both training and testing datasets (Table), measured LVEF values (mLVEF, %) and an-notated LVEF values (aLVEF, %) were found to be statistically highly correlated (ρ≥0.89); moreover, median mLVEF value was not statistically different from median aLVEF value. Eventually, mean absolute error (MAE, %) was ≤5%. Thus, DL-LVEF provided objective and accurate LVEF measurement. Future DL-LVEF evolutions will provide segmenta-tion of other cardiac anatomical structures and, thus, will allow meas-urement of other clinically relevant cardiac indexes.