Abstract Funding Acknowledgements Type of funding sources: None. Background Evaluation of right ventricle (RV) function has clinical value when investigating patients with symptomatic heart failure. However, the assessment of RV function using two-dimensional echocardiography (Echo) is operator-dependent and is time-consuming. Technological advances have enabled the use of artificial intelligence (AI) based algorithms for functional analysis with high reproducibility, facilitated by manufacturer-neutral platform. Purpose We sought to determine the feasibility of using an AI-based application to measure RV function in patients referred for diagnostic evaluation of heart failure, and to compare the results with operator-generated (Human) measurements in order to assess the accuracy and efficiency of the AI. Methods We conducted a study on 190 consecutive patients, comparing the analysis times and accuracies of tricuspid annular plane systolic excursion (TAPSE) and fractional area change (FAC) measurements using both Human and AI-based methods on three consecutive heart beats of the same Echo clip. The participants manually measured TAPSE in M-mode and FAC by tracking the RV endocardium in diastole and systole, while the AI automatically assessed these parameters in a single apical RV-centered 4-chamber view. The results of these measurements were then compared to determine the accuracy and efficiency of the AI. Results The measurement feasibility by the AI was 92% (n=175) on the RV clips. The main reasons for exclusion were poor image quality and insufficient representation of the RV free wall. The analysis time for TAPSE and FAC was significantly shorter for the AI compared to the Human (14±4 seconds vs. 101±21 seconds, p<0.001). Figure 1 shows that there was a significant correlation between TAPSE and FAC measurements made by AI and Human (R=0.66 and 0.64, p<0.001), but there was a mean bias of 3.3 mm (±23.7 mm) for TAPSE and 3.9% (±16.7%) for FAC between the two methods. Conclusions The AI-based application demonstrated good feasibility and significantly reduced analysis time for measuring TAPSE and FAC in patients referred for diagnostic evaluation of heart failure. However, the measurement bias between AI and Human was not negligible, warranting further investigation to determine the cause of this discrepancy and whether this AI-based application is an alternative to the manual method for assessing RV function.