Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms (DECISION)

Inclusion Criteria: * Patients with Suspected HF (Group S): * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative). * Age over 65 years (i.e., 65 included). * Presence of symptoms and/or signs typical of Heart Failure (defined by the European Society of Cardiology, ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles/legs, and/or palpitations. * Patients at Risk of Heart Failure due to the presence of cardiovascular (Group R): * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative). * Age over 65 years (i.e., 65 included). * Absence of symptoms and/or signs typical of Heart Failure (defined by the ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles/legs, and/or palpitations. * Presence of at least 1 ACC/AHA Heart Failure risk factor, including hypertension, cardiovascular disease (atrial fibrillation, coronary heart disease or stroke), diabetes, obesity, exposure to cardiotoxic agents, genetic variant for cardiomyopathy, or family history of cardiomyopathy that requires an ECG test for any reason in a primary care center or with an indication of a regular health examination where an ECG is included. Exclusion Criteria: * Unwillingness or inability to sign the written informed consent. * Previous Heart Failure diagnosis. * Unavailability or suboptimal quality ECG.