# Artificial intelligence–enhanced electrocardiography for identifying subclinical left ventricular dysfunction in hypertensive individuals: a comprehensive clinical evaluation

## TL;DR

This study shows that AI-enhanced ECG can detect early heart issues in people with high blood pressure, even when standard tests appear normal.

## Contribution

The study demonstrates AI-ECG's ability to detect subclinical LV dysfunction in hypertensive individuals using GLS as a reference.

## Key findings

- AI-ECG scores strongly correlated with GLS values (r = -0.63) and differed significantly between normal and abnormal GLS groups.
- AI-ECG showed robust diagnostic accuracy with an AUC of 0.86 for detecting subclinical LV dysfunction.
- The AI-ECG score remained independently associated with subclinical LV dysfunction after adjusting for multiple clinical factors.

## Abstract

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.

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.

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; p < 0.001). GLS values demonstrated a strong negative association with AI-ECG scores (r = –0.63). ROC analysis showed robust diagnostic ability with an AUC of 0.86 (95% CI: 0.82–0.89). In multivariable logistic regression adjusting for LV mass index, E/e′, age, and hypertension duration, the AI-ECG probability score remained independently associated with subclinical LV dysfunction (adjusted OR 1.12 per 0.1 increase, 95% CI 1.07–1.18; p < 0.001).

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.