The Reliability of Automated Three-Dimensional Echocardiography-HeartModelA.I. Versus 2D Echocardiography Simpson Methods in Evaluation of Left Ventricle Volumes and Ejection Fraction in Patients With Left Ventricular Dysfunction

Background: Two-dimensional echocardiography (2DE) Simpson methods is the most frequently used imaging modality to assess Left ventricular ejection fraction (LVEF). LVEF is an important predictor of morbidity and mortality in a wide range of patients and clinical scenarios. Despite its importance in prognosis and clinical decision making, most echocardiography laboratories currently determine EF primarily by visual estimation, which is highly experience-dependent and sensitive to intra- and inter-observer variability and suboptimal accuracy and repeatability. Over the last decade, 3-dimensional echocardiography (3DE) has become increasingly implemented in clinical practice. The automated 3D HeartModelA.I. tracks every frame over the cardiac cycle using 3D speckle technology. HeartModelA.I. is a fully automated program that simultaneously detects LA and LV endocardial surfaces using an adaptive analytics algorithm that consists of knowledge-based identification of initial global shape and orientation followed by patient-specific adaptation. Objective: The objective of the study was to compare the automated 3D HeartModelA.I echocardiography and 2D Simpson methods echocardiography in evaluation of the left ventricular ejection fraction and left ventricular volumes in patients with left heart dysfunction. Methods: The study prospectively enrolled 165 patients with symptoms of LV dysfunction (ischemic or nonischemic) and New York Heart Association (NYHA) functional class I-III, referred for an echocardiographic study to evaluate the LV volumes and LV ejection fraction (LVEF) during the period from March 2020 to March 2022. Echocardiographic images were acquired by experienced echocardiographers using a commercially available Philips EPIQ machine (Koninklijke Philips Ultrasound, USA) equipped with X5-1 Matrix probe for 2DE and DHM 3DE acquisitions, respectively. Results: 2D Simpson methods echocardiography results for estimated LVEF were 38.43 ± 1.70 in patients with NYHA class I-II, 30.53 ± 1.60 in patients with NYHA class III. Using 3D Heart Model, LVEF were 38.23 ± 1.71 in patients with NYHA class I-II and 30.27 ± 1.50 in patients with NYHA class III. The results of 2D Simpson methods echocardiography for estimated LVEDVi in NYHA class I-II and NYHA class III were 99.06 ± 6.36 ml/m2, 121.96 ± 2.93 ml/m2 respectively, LVESVi were 60.91 ± 3.91 ml/m2, 84.74 ± 2.70 ml/m2 respectively, for 3D Heart Model, LVEDVi in NYHA class I-II and NYHA class III were 100.07 ± 6.72, 121.38 ± 3.01 ml/m2 respectively, LVESVi were 61.75 ± 3.94 ml/m2, 84.73 ± 2.33 ml/m2 respectively. 2DE measurement of LV volumes and EF was completed in 6.1 ± 0.8 min. per patient. 3DE HeartModelA.I acquisition and analysis in most patients was completed in <3.2 min., an average time of 2.9 ± 1.3 min. per patient. The result of our study shows that the 3D HeartModelA.I. is a reliable and robust method for LVEF and LV volume analysis, which has similar results to 2D echocardiography performed by experienced sonographers. In this study, we found that 3DE DHM fully automated tool is also significantly faster than 2DE analysis and thus can help overcome the time-consuming nature and its present a strong argument for its incorporation into the clinical workflow. In this study, we found that 3DE DHM fully automated tool is also significantly faster than 2DE analysis and thus can help overcome the time-consuming nature and its present a strong argument for its incorporation into the clinical workflow. Conclusion: 3D DHM provides fast and accurate LV volumes and LVEF quantitation, as it avoids geometric assumptions and left ventricular foreshortening, has better reproducibility and has incremental value to predict adverse outcomes in comparison with conventional 2DE. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome.