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Left ventricular (LV) function is an important factor in terms of patientmanagement, outcome, and long-term survival of patients with heart disease. Themost recently published clinical guidelines for heart failure recognise thatover reliance on only one measure of cardiac function (LV ejection fraction) asa diagnostic and treatment stratification biomarker is suboptimal. Recentadvances in AI-based echocardiography analysis have shown excellent results onautomated estimation of LV volumes and LV ejection fraction. However, fromtime-varying 2-D echocardiography acquisition, a richer description of cardiacfunction can be obtained by estimating functional biomarkers from the completecardiac cycle. In this work we propose for the first time an AI approach forderiving advanced biomarkers of systolic and diastolic LV function from 2-Dechocardiography based on segmentations of the full cardiac cycle. Thesebiomarkers will allow clinicians to obtain a much richer picture of the heartin health and disease. The AI model is based on the 'nn-Unet' framework and wastrained and tested using four different databases. Results show excellentagreement between manual and automated analysis and showcase the potential ofthe advanced systolic and diastolic biomarkers for patient stratification.Finally, for a subset of 50 cases, we perform a correlation analysis betweenclinical biomarkers derived from echocardiography and CMR and we show excellentagreement between the two modalities.

AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography