scholarly journals 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

2022 ◽  
Author(s):  
Ying Zhu ◽  
Yuwei Bao ◽  
Kangchao Zheng ◽  
Wei Zhou ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Magnetic Resonance ◽  
Quantitative Assessment ◽  
Three Dimensional ◽  
Ventricular Size ◽  
Multiple Parameters ◽  
Right Ventricular Size ◽  
Dimensional Echocardiography ◽  
Three Dimensional Echocardiography ◽  
And Function

scholarly journals 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

2021 ◽  
Author(s):  
Ying Zhu ◽  
Yuwei bao ◽  
Kangchao Zheng ◽  
Wei Zhou ◽  
Zhang Jun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
Systolic Volume ◽  
Multiple Parameters ◽  
End Systolic Volume ◽  
Dimensional Echocardiography ◽  
Three Dimensional Echocardiography ◽  
And Function

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

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