Artificial intelligence for left ventricular strain.

Abstract

Purpose of review: To review recent advances in artificial intelligence (AI) for left ventricular (LV) strain echocardiography, with emphasis on studies published during the preceding 18 months, and to assess current evidence for measurement performance, workflow integration, emerging AI-based approaches, disease-specific applications and barriers to widespread clinical adoption.

Recent findings: The most mature evidence relates to automated global longitudinal strain (GLS) measurement, where recent studies show high feasibility, improved reproducibility and reduced operator dependence compared with conventional analysis. AI-based tools have also moved beyond retrospective post-processing towards real-time acquisition support and workflow integration, with evidence of shorter analysis time and more standardized image acquisition. Emerging approaches suggest a shift from single-task GLS automation towards multitask and phenotyping-oriented models in which strain is integrated with broader echocardiographic interpretation. Disease-specific applications are most studied in acute ischaemic heart disease and cardio-oncology, where AI-based strain has shown promise for risk stratification, surveillance and clinical triage. However, agreement with conventional methods remains imperfect and varies substantially across models, decision thresholds are uncertain, and prospective evidence for improved clinical outcomes is still limited.

Summary: AI for LV strain is evolving from single-task automated measurement towards broader support for reproducibility, workflow integration and clinical interpretation. Current priorities are robust external validation, transparent reporting of feasibility and unsuccessful analyses, and prospective evaluation of clinical utility and workflow impact in routine practice, with clinician oversight as a critical component of safe implementation.