Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography

Aims: Stress echocardiography (SE) is widely used for assessing coronary artery disease, but volumetric chamber analysis during SE is limited by time-consuming manual tracings and operator-dependent variability. Automated evaluation may overcome these barriers and enhance efficiency. Methods and results: This multi-centre study included 240 participants undergoing pharmacological SE for ischaemic heart disease evaluation from five sites in four countries. SE imaging data from apical four-chamber and two-chamber views were acquired during rest and stress phases. Expert cardiologists manually traced endocardial borders for left ventricular (LV), left atrial (LA) and right ventricular (RV), right atrial (RA) areas, which were compared to machine learning (ML) derived measurements. Image quality was categorized as optimal, good, fair, or poor, and its influence on ML performance was analysed. Statistical methods included Intraclass Correlation Coefficients (ICCs), Bland-Altman testing, and within-patient coefficient of variation. The yield of the ML algorithm demonstrated consistency across rest and stress phases. It demonstrated strong agreement with cardiologists for LV and LA volumes, with ICCs ranging from 0.84 to 0.93 across rest and stress conditions. RA and RV areas measurements showed moderate correlations, with better agreement at rest than during stress phases. Image quality significantly influenced ML performance, as poor-quality images reduced diagnostic yield. Conclusion: AI-driven volumetric analysis is a reliable method for quantifying left-sided heart chambers during pharmacological SE, with results closely matching expert measurements. Moderate reliability for right-sided chambers highlights the need for high-quality imaging and standardized protocols. AI integration may streamline SE workflows and support improved clinical decision-making.