Artificial Intelligence-Based Echocardiographic Assessment of Diastolic Dysfunction: A Systematic Review

Accurate non-invasive assessment of left ventricular diastolic dysfunction and filling pressures is central to the diagnosis and management of heart failure, particularly heart failure with preserved ejection fraction (HFpEF). Despite widespread use of transthoracic echocardiography, guideline-based algorithms are frequently indeterminate, variably reproducible, and difficult to apply consistently in routine practice. Artificial intelligence (AI) has emerged as a potential strategy to standardize measurement, integrate multiparametric echocardiographic data, and reduce diagnostic uncertainty. We performed a systematic review of diagnostic accuracy studies evaluating AI or machine-learning approaches applied to echocardiography for automated assessment of diastolic function or estimation of left ventricular filling pressures. Databases were searched from inception to July 7, 2026. Reference standards included invasive hemodynamics or guideline-based diastolic classification. Risk of bias and applicability were assessed using QUADAS-3. Fourteen studies were included, with development samples and validation or test cohorts reported separately. Validation ranged from small held-out invasive cohorts to large independent external datasets. Against invasive hemodynamic reference standards, reported AUCs ranged from 0.761 to 0.883, reflecting heterogeneous hemodynamic targets, thresholds, and validation designs. Studies evaluating agreement with guideline- or expert-derived classifications and automated replication of individual diastolic parameters generally reported high discrimination, although these represented distinct clinical tasks and varied substantially in the independence and scale of validation. Several studies demonstrated external or prospective validation, but robust evaluation against invasive hemodynamics remained limited. Given substantial methodological and clinical heterogeneity, findings were synthesized descriptively and no summary estimate was calculated. Despite promising diagnostic discrimination in retrospective studies, AI-based echocardiographic diastolic assessment remains limited by heterogeneous reference standards, variable validation approaches, and an absence of prospective interventional clinical-impact studies and limited prospective multicenter validation against invasive hemodynamics, precluding routine implementation at this stage.