Echocardiography. 2022 Jan 15. doi: 10.1111/echo.15292. Online ahead of print.
ABSTRACT
AIMS: This study aimed to explore the validation and the diagnostic value of multiple right ventricle (RV) volumes and functional parameters derived from a novel artificial intelligence (AI)-based three-dimensional echocardiography (3DE) algorithm compared to cardiac magnetic resonance (CMR).
METHODS AND RESULTS: A total of 51 patients with a broad spectrum of clinical diagnoses were finally included in this study. AI-based RV 3DE was performed in a single-beat HeartModel mode within 24 hours after CMR. In the entire population, RV volumes and right ventricular ejection fraction (RVEF) measured by AI-based 3DE showed statistically significant correlations with the corresponding CMR analysis (p < 0.05 for all). However, the Bland-Altman plots indicated that these parameters were slightly underestimated by AI-based 3DE. Based on CMR derived RVEF < 45% as RV dysfunction, end-systolic volume (ESV), end-systolic volume index (ESVi), stroke volume (SV), and RVEF showed great diagnostic performance in identifying RV dysfunction, as well as some non-volumetric parameters, including tricuspid annular systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strains (LS) (p < 0.05 for all). The cutoff value was 43% for RVEF with a sensitivity of 94% and specificity of 67%.
CONCLUSION: AI-based 3DE could provide rapid and accurate quantitation of the RV volumes and function with multiple parameters. Both volumetric and non-volumetric measurements derived from AI-based 3DE contributed to the identification of the RV dysfunction.
PMID:35034377 | DOI:10.1111/echo.15292
