ReviewEuropean heart journal. Digital health2026
Diagnostic performance of artificial intelligence-enabled electrocardiography for pulmonary hypertension: a systematic review and meta-analysis.
Review in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
Pulmonary hypertension (PH) is a progressive, under-recognized condition with substantial morbidity and mortality. Its non-specific presentation and multimodal diagnostic pathway contribute to delayed diagnosis. Artificial intelligence (AI)-enabled electrocardiography (ECG) may offer a scalable approach to case finding. This PRISMA-DTA systematic review and meta-analysis was registered in PROSPERO (CRD420251241142). MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore were searched through July 2026, with Google Scholar and citation searching used as supplementary sources. Eligible studies evaluated AI-enabled ECG in adults against right-heart catheterization (RHC)-confirmed PH or echocardiographic PH-related phenotypes. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2. One ECG-only area under the receiver operating characteristic curve (AUROC) per study was pooled using a random-effects model. Seven retrospective studies were included; six contributed to meta-analysis. The pooled AUROC was 0.885 (95% CI 0.855-0.915), with very high heterogeneity (I² = 98.68%;
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