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Quantitative Assessment of Right Ventricular Size and Function with Multiple Parameters from Artificial Intelligence-Based Three-Dimensional Echocardiography: A Comparative Study with Cardiac Magnetic Resonance

Abstract Aims: This study aimed to explore the validation and the
diagnostic value of multiple right ventricle (RV) volumes and functional
parameters parameters derived from a novel artificial intelligence
(AI)-based three-dimensional echocardiography (3DE) algorithm compared
to cardiac magnetic resonance (CMR). Methods and Results: 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. Whether in the entire population or the
patients with moderate and poor image quality, RV volumes and right
ventricular ejection fraction (RVEF) measured by AI-based 3DE showed a
statistically significant correlation with the corresponding CMR
analysis (P<0.05 for all). 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 (EDVi), 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), RV septum 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 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, even in the
patients without excellent image quality of RV 3DE. Keywords: artificial
intelligence, three-dimensional echocardiography, right ventricle,
multiple parameters