Concordance of left ventricular volumes and function measurements between two human readers, a fully automated AI algorithm, and the 3D heart model.

Abstract

Echocardiography is essential in cardiovascular medicine for screening, diagnosis, and monitoring. Artificial intelligence (AI) has the potential to improve echocardiography by reducing variability and analysis time. While 3D echocardiography is becoming more accurate, 2D imaging still dominates clinical care. We aimed to evaluate agreement in measures of left ventricular (LV) volumes and function between human readers, a fully automated AI 2D algorithm, and the 3D Heart Model. A retrospective analysis was conducted on 109 patients who underwent 2D and 3D transthoracic echocardiography. LV end-diastolic and end-systolic volumes (LVEDV, LVESV) and ejection fraction (LVEF) were measured by two operators, a commercially available AI algorithm (US2ai), and the 3D Heart Model. Global longitudinal strain (GLS) was measured by the integrated semi-automated software and the AI algorithm. Outcomes included measures of agreement [bias, limit of agreement and Pearson's correlation (R)]. For LV volume measurements, the AI algorithm was strongly correlated with the average of the human operators (r = 0.89 for LVEDV and r = 0.92 for LVESV), which was higher than between the operators (r = 0.74 and r = 0.84, respectively, p < 0.01). The same trend was seen for measures of reliability with respect to LVEDV, but not LVESV. AI demonstrated comparable performance to human operators in measuring LVEF, while the 3D Heart Model had a weaker correlation and reliability compared with human operators and AI measurements. The correlation between human operators and AI for GLS was only moderate. This study demonstrates AI-based echocardiography as a promising tool for accurately assessing LV volumes and LVEF in clinical practice. AI-based measures demonstrated a significantly lower inter-operator variability, thereby improving the consistency and reliability of these assessments. Moreover, AI may prove particularly effective for conducting retrospective bulk analyses, offering a valuable tool for comprehensive evaluations of past data.