Heart failure with preserved ejection fraction (HFpEF), characterized by a left ventricular ejection fraction (LVEF) ≥ 50%, affects approximately 3 million individuals in the United States and up to 32 million globally.¹

Despite its high prevalence, HFpEF remains underdiagnosed, accounting for nearly half of all heart failure cases. It is associated with significant morbidity, frequent hospitalizations, and an annual mortality rate of around 15%. Early diagnosis and timely intervention are essential for optimizing management, improving patient outcomes, and reducing healthcare costs.^2,3

Diagnosing HFpEF can be challenging due to its nonspecific symptoms and signs, often derived from patient history and physical examination. The electrocardiogram (ECG), however, is a valuable, noninvasive, and cost-effective tool in this context.⁴ As pathophysiological changes in the heart influence its electrical activity, ECG data can provide crucial insights for early HFpEF detection.^5,6

Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, have significantly improved diagnostic capabilities, enabling more accurate analysis of complex data.⁷ When integrated with ECG, AI may have the potential to overcome diagnostic challenges in HFpEF.⁸ This systematic review and meta-analysis aims to evaluate the diagnostic performance of artificial intelligence-based electrocardiogram (AI-ECG) algorithms in detecting HFpEF, providing a comprehensive assessment of their clinical utility.

We carried out this systematic review and meta-analysis following the guidelines outlined in the Cochrane Collaboration Handbook for Systematic Review of Interventions and the PRISMA Statement.^9,10 The protocol for the prospective meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the protocol number CRD420251002805.

Our systematic review initially yielded 1,803 results. After removing duplicates and screening based on title and abstract, 20 full-text articles were reviewed for possible inclusion. Finally, three observational studies fulfilled our inclusion criteria and were included in the analysis, comprising a pooled population of 171,073 patients, of whom 114,383 (66.86%) were in the HFpEF group with a mean age of 65.86 ± 4.5 years. Details of the study selection are shown in Figure 1, and baseline characteristics are displayed in Table 1.

The meta-analysis revealed that AI-ECG demonstrated an estimated diagnostic sensitivity of 0.89 (95% CI: 0.64 to 0.97; Figure 2a) and a pooled specificity of 0.80 (95% CI: 0.65 to 0.89; Figure 2b) for HFpEF. Considerable heterogeneity was observed among the included studies (sensitivity: I² = 99.4%; specificity: I² = 99.8%). The summary receiver operating characteristic (SROC) analysis yielded an AUC of 0.89 (Figure 3). The main findings are shown in the Central Illustration.

Sensitivity analyses were performed using a leave-one-out approach (Supplementary Material Figures S1 and S2). The results demonstrated that the pooled estimates for both sensitivity and specificity remained consistent when each study was sequentially omitted, indicating that no single study had a disproportionate impact on the overall findings.

In this systematic review and meta-analysis of three studies, comprising 171,073 patients (66.86% with HFpEF), we assessed the diagnostic performance of AI-ECG models for detecting HFpEF, using echocardiography as the reference standard. The pooled data yielded the following main findings: (1) AI-ECG exhibited moderate to high sensitivity (0.89, 95% CI: 0.64 to 0.97) and specificity (0.80, 95% CI: 0.65 to 0.89); and (2) an overall AUC of 0.89 (95% CI: 0.85 to 0.92). These results show a strong discriminating capacity and a good balance between sensitivity and specificity. Moreover, the AUC indicates moderate to high accuracy for diagnosis according to the model, thus providing options for early detection and risk stratification regarding HFpEF.

Our results coincide with other recent studies showing that deep learning models can efficiently detect minor changes in ECG waveform, enabling earlier identification of HFpEF as compared to conventional clinical ECG interpretation.¹³ Notably, another study¹⁴ reported a sensitivity of up to 99% when using a convolutional neural network applied to standard 12-lead ECGs in patients at risk for diastolic dysfunction, emphasizing the impressive rule-out capabilities of AI-ECG. These high-sensitivity models, however, may cause a higher false-positive rate, which emphasizes the need for confirmatory echocardiogram in ordinary clinical use.

Beyond basic diagnostic accuracy, AI-based ECG techniques offer promising prognostic potential. A recent study¹⁵ revealed that patients with "false-positive" AI-ECG results were more likely to develop overt LV dysfunction on later follow-up, implying that even negative echocardiograms should require more attention if detected by AI. A subsequent study showed that combining AI-ECG outputs with basic clinical data might outperform some standard risk algorithms for asymptomatic LV dysfunction,¹⁶ hence validating the theory that advanced computational tools detect subclinical alterations missed by typical ECG interpretations. Building on these observations, integrating clinical parameters—such as demographics, comorbidities, or laboratory biomarkers—with ECG-derived AI outputs may further improve diagnostic accuracy for HFpEF, resulting in more comprehensive and individualized prediction models. Growing data thus point to AI-ECG as a readily available triage tool, identifying people who might benefit from tailored therapy regimens and earlier echocardiographic investigation.

Our meta-analysis further shows that, for AI-ECG, the pooled estimates stayed consistently favorable even though different investigations used heterogeneous algorithms and datasets. This constancy emphasizes, even across different populations and healthcare settings, the strength of contemporary neural network designs for ECG interpretation.¹⁷ Furthermore, subgroups of patients with questionable EF should particularly benefit from AI-driven screening since advanced models have shown the capacity to identify minor diastolic anomalies earlier than standard techniques.¹⁸ These results highlight the possible synergy between AI-ECG and conventional therapeutic processes, therefore supporting our study's contribution to the existing body of knowledge.

Ferreira et al.¹⁹ recently conducted a meta-analysis on the diagnostic accuracy of AI-ECG specifically for HFrEF (LVEF < 40%) rather than HFpEF. Their study similarly revealed strong pooled sensitivity (around 0.84) and specificity (about 0.89), despite concentrating on a clear EF cutoff, therefore supporting the general theory that AI-ECG may consistently identify underlying systolic dysfunction. Although their results highlight the value of AI-ECG in early diagnosis and risk classification for HFrEF, our meta-analysis adds the evidence regarding HFpEF, a clinical entity sometimes missed in standard tests. These two meta-analyses taken together show that AI-ECG has promise as a flexible, reasonably affordable screening tool spanning the range of EF—whether moderately lowered or preserved—highlighting the general.

Even with these promising findings, this study has some limitations. We tried to reduce selection bias by only adding studies with echocardiographic confirmation. Yet, residual confounding could still be introduced by the natural variation in ECG acquisition techniques and AI architectures. Furthermore, although we limited our meta-analysis to three trials satisfying tight inclusion criteria, the somewhat limited body of published evidence might constrain generalizability. Finally, none of the given datasets offered detailed cost-effectiveness or prospective outcome data; therefore, more studies are required to define actual therapeutic effects and clinical impact. Despite these constraints, our pooled results remain consistent with an increasing body of evidence, supporting AI-ECG as a reliable, non-invasive modality for HFpEF screening and early detection.

This meta-analysis demonstrates that AI-ECG algorithms exhibit moderate to high sensitivity, specificity, and diagnostic accuracy in detecting HFpEF, highlighting their potential as a valuable tool for early screening and diagnosis. These findings support the integration of AI-ECG algorithms into clinical practice as a promising approach for HFpEF detection, especially when combined with relevant clinical data to further enhance diagnostic accuracy. However, further large-scale prospective studies are necessary to validate these results and establish the optimal integration of AI-ECG technology across diverse healthcare settings.