Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients.

AIMS: Global longitudinal strain (GLS) is essential for the early detection of cancer therapy-related cardiac dysfunction (CTRCD). A fully automated echocardiographic analysis system using artificial intelligence (AI) may improve workflow efficiency in cardio-oncology. We sought to evaluate the feasibility and diagnostic performance of a fully automated AI-based echocardiographic system in breast cancer patients receiving cardiotoxic chemotherapy. METHODS AND RESULTS: In this prospective observational study, patients with breast cancer undergoing anthracyclines and/or HER2-targeted therapy between January 2022 and June 2025 were enrolled. Transthoracic echocardiography was performed at baseline and every 12 weeks. GLS was measured manually by two experts and automatically by a fully automated AI-based analysis system. A total of 92 patients (456 echocardiographic studies) were analysed. AI-derived GLS values were significantly lower than expert measurements (17.7 ± 2.9% vs. 18.4 ± 2.8%, P = 0.007). Correlation and agreement between the two methods were moderate (R = 0.64, intraclass correlation coefficient = 0.63). On linear mixed-effects modelling, longitudinal changes in GLS were not significantly different between methods (P = 0.72). GLS-based CTRCD was detected in 31.5% of patients by experts and 34.8% by AI (P = 0.58), with similar detection timing (P = 0.47). Diagnostic agreement was substantial (κ = 0.68, P < 0.001). CONCLUSION: The fully automated AI-based echocardiographic system demonstrated acceptable agreement and diagnostic performance for GLS assessment and showed a similar ability to track temporal relative GLS changes and identify CTRCD. However, systemic underestimation of absolute GLS values may contribute to threshold-based classification discordance in borderline cases.