Key result
Artificial intelligence-based 3DE significantly correlated with CMR for right ventricular assessment (p<0.05), with an RVEF cutoff of 43% identifying RV dysfunction with 94% sensitivity and 67% specificity.
Why the study?
To evaluate the validation and diagnostic value of multiple RV volume and functional parameters from a novel AI-based 3DE algorithm compared with CMR.
Does AI-based 3DE accurately measure RV volumes and function compared to CMR in patients with various clinical diagnoses?
Observational (n=51)
Does AI-based 3DE accurately measure RV volumes and function compared to CMR in patients with various clinical diagnoses?
p-value: p=<0.05
AI-based 3D echocardiography provides accurate and rapid quantification of right ventricular volumes and function, offering a viable alternative to CMR for identifying RV dysfunction.
AI 3DE RV parameters feasible vs CMR in small cohort; leaves open larger validation before clinical adoption.
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.
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Zhu et al. (2022) conducted an observational in Various clinical diagnoses (n=51). Artificial intelligence-based three-dimensional echocardiography (3DE) vs. Cardiac magnetic resonance (CMR) was evaluated on Correlation of RV volumes and RVEF with CMR, and diagnostic performance for RV dysfunction (p=<0.05). Artificial intelligence-based 3DE significantly correlated with CMR for right ventricular assessment (p<0.05), with an RVEF cutoff of 43% identifying RV dysfunction with 94% sensitivity and 67% specificity.
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