Composite Artificial Intelligence-Enabled Electrocardiogram for Detection and Prediction of Structural Heart Disease

Artificial intelligence–enabled electrocardiography for detection of left ventricular diastolic dysfunction: a systematic review and meta-analysis

Artificial intelligence-enhanced electrocardiography demonstrated good diagnostic performance for detecting LVDD and may support future rule-out or risk-enrichment strategies in selected populations, however, current evidence remains insufficient to support routine clinical implementation.

Artificial intelligence–enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models

Adding AI-ECG signals to PREVENT-HF improves near-term heart-failure risk discrimination and reclassification, though without demonstrated benefit on clinical outcomes such as heart-failure hospitalization or mortality.

Artificial Intelligence-Enabled Electrocardiography Model for Left Ventricular Systolic and Diastolic Dysfunction Predict Incident Atrial Fibrillation in Patients with Sinus Rhythm: A Time-Dependent Analysis

Background/Objectives: Artificial intelligence-enabled electrocardiography (AI-ECG) can infer left ventricular systolic dysfunction (LVSD) and diastolic dysfunction (LVDD) from a standard 12-lead tracing. Whether these structural AI-ECG scores predict incident atrial fibrillation (AF) in patients in sinus rhythm, and how their predictive value changes over time, is unclear. Methods: In a retrospective single-center cohort of patients with a sinus-rhythm index ECG, AI-ECG LVSD and LVDD scores were derived and dichotomized at prespecified cutoffs (LVSD ≥ 9.7; LVDD ≥ 20.8). The outcome was incident AF, assessed within 30, 90, 180, 270, and 365 days. Discrimination was quantified by the AUROC. Because the proportional hazards assumption was violated, the time course of risk for the joint LVSD × LVDD classification was characterized with window-specific Cox, Aalen additive hazards, and restricted mean survival time analyses. Results: Among 19,593 index ECGs from 18,984 patients, 1190 (6.07%) were followed by incident AF within one year. One-year incidence rose from 4.1% (both negative) to 9.8% (LVSD-positive only), 20.1% (LVDD-positive only), and 19.9% (both positive). Discrimination was highest for early events and attenuated over time, more steeply for LVSD (AUROC 0.803 at 30 days to 0.713 at 365 days) than LVDD (0.817 to 0.764). Combining AI-ECG LVSD and LVDD models, the excess risk conferred by dual positivity was concentrated in the first weeks after the index ECG and, consistently across cumulative Cox, Aalen additive hazards, and restricted mean survival time analyses, converged with that of isolated LVDD positivity by one year. Conclusions: Structural AI-ECG LVSD and LVDD scores from a single sinus-rhythm ECG predict incident AF in a time-dependent manner, with the strongest performance shortly after acquisition and more durable discrimination for LVDD. A single AI-ECG may help target short-term AF surveillance, particularly in patients with combined systolic–diastolic dysfunction.

ECG-based longitudinal risk prediction across diseases and organ systems

Prediction of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Based Electrocardiogram Analysis Model in Patients Presenting to the Emergency Department

The AI-ECG model accurately identified LVSD in ED patients in this cohort despite heterogeneous acquisition conditions and retained good discrimination in hemodynamically unstable subgroups, although findings in the smaller hypotensive subgroup should be interpreted as exploratory.

Utility of ECG-Vision for Surveillance of Left Ventricular Dysfunction in Patients With Hypertrophic Cardiomyopathy Initiated on Mavacamten.

In this single-center exploratory cohort of patients with obstructive hypertrophic cardiomyopathy receiving mavacamten, ECG-Vision left ventricular demonstrated high observed sensitivity and negative predictive value for TTE-defined LVSD, although estimates were imprecise because LVSD events were infrequent.