ArticleFrontiers in cardiovascular medicine2026
Artificial intelligence-enhanced electrocardiography for identifying subclinical left ventricular dysfunction in hypertensive individuals: a comprehensive clinical evaluation.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
Background: Subclinical left ventricular (LV) impairment-characterized by reduced global longitudinal strain (GLS) despite normal left ventricular ejection fraction (LVEF)-is frequently encountered in hypertensive patients. While speckle-tracking echocardiography is the standard method for detecting early myocardial dysfunction, it is not universally available. Artificial intelligence-enhanced electrocardiography (AI-ECG) has emerged as a promising tool capable of uncovering subtle electrical patterns linked to early myocardial impairment. This study investigates the diagnostic capability of AI-ECG for detecting GLS-defined subclinical LV dysfunction. Methods: In this retrospective analysis, 348 hypertensive adults who underwent both ECG and echocardiography within the same clinical visit (2022-2024) were evaluated. Subclinical LV dysfunction was defined as LVEF ≥50% and GLS > -18%.A convolutional neural network-based AI algorithm generated an AI-ECG probability score (range 0-1) representing the likelihood of LV dysfunction. Statistical analyses included correlation testing, regression modeling, and ROC curve evaluation. Results: Subclinical LV dysfunction was identified in 134 participants (38.5%). The AI-ECG probability score differed markedly between the abnormal GLS group and the normal GLS group (0.61 ± 0.20 vs. 0.29 ± 0.18; Conclusion: AI-ECG accurately detects GLS-defined subclinical LV dysfunction in hypertensive adults and may serve as an accessible tool for early risk stratification in routine clinical settings.
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