Fully Automatic AI-Based Quantification of LV Mass in Echocardiography

BACKGROUND: Accurate assessment of left ventricular (LV) myocardial mass is critical for guiding treatment decisions. Measurements based on echocardiography are limited by operator variability. Artificial intelligence (AI)-based methods promise improved precision and reproducibility, but they require validation against high-resolution reference standards such as computed tomography (CT). OBJECTIVES: The aim of this study is to compare the accuracy of AI-based vs expert manual echocardiography LV mass measurements using CT as the reference and to evaluate real-world test-retest reliability. METHODS: In 218 patients undergoing echocardiography and CT, the authors analyzed LV mass, interventricular septal diameter (IVSD), end-diastolic diameter, posterior wall diameter (PWD), and end-diastolic volume. LV mass was assessed by the conventional linear method as well as a novel hybrid method combining end-diastolic tracings with mean wall thickness. AI- and expert-based (Expert) echocardiographic measurements were compared with CT using intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE). Increased relative wall thickness (RWT >0.42;+) and abnormal LV mass was evaluated using receiver-operating characteristic analysis (area under the curve [AUC]). Test-retest reliability was assessed in a bedside cohort (n = 40) using coefficient of variation. RESULTS: AI echocardiography showed strongest agreement with CT for LV mass assessment using the hybrid method (ICC: 0.76; MAPE: 0.16) and lower agreement for the linear method (ICC: 0.49; MAPE: 0.44). Nevertheless, compared with Expert echocardiography, AI reduced measurement error by ∼20%, mainly due to more consistent IVSD and PWD measurements, and reclassified 34% of RWT+ cases. Diagnostic accuracy for detecting increased LV mass was higher with AI echocardiography than with Expert echocardiography (AUC: 0.78 vs 0.71 for linear; 0.84 vs 0.77 for hybrid). Test-retest reliability was highest with AI echocardiography for both mass methods (coefficient of variation: 7.8% and 8.1%), and reproducibility of manual assessment varied with operator skills. CONCLUSIONS: AI echocardiography provides superior accuracy, diagnostic performance, and reproducibility for LV mass assessment compared with expert evaluation, supporting its clinical integration to improve standardization in cardiac imaging.