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TRUVACE RECORD VERSION
record: TRV-2026-1837
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T18:13:14.134253Z
status: published
lens: g_space
sector: health
headline: External validation of artificial intelligence-guided electrocardiogram (AI-ECG) in detecting asymptomatic left ventricular dysfunction: A systematic review and meta-analysis
dek: Aim: Asymptomatic left ventricular dysfunction (LVD) represents a key and frequently under-recognized stage in the heart failure (HF) continuum. Early identification enables targeted prevention and timely initiation of evidence-based therapies. Artificial intelligence-enabled electrocardiography (AI-ECG) may provide a scalable alternative to imaging for LVD detection. This systematic review synthesized evidence from exclusively externally validated AI-ECG studies evaluating the detection of asymptomatic LVD, wit…
gain_title: Across externally validated studies, AI-ECG discriminated asymptomatic left ventricular systolic dysfunction well, with performance broadly comparable to symptomatic populations, suggesting a scalable alternative to imaging.
problem_title: (none)
trace_subject: (none)
gain_reading: Across externally validated studies, AI-ECG discriminated asymptomatic left ventricular systolic dysfunction well, with performance broadly comparable to symptomatic populations, suggesting a scalable alternative to imaging.
gain_evidence: Discriminatory performance of AI-ECG in asymptomatic LVSD is broadly comparable to symptomatic populations
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled externally validated studies of AI-enabled electrocardiography for finding left ventricular dysfunction. From 3,587 records, it included 12 asymptomatic and 30 symptomatic studies covering 538,735 participants, with the pooled AUROC as the main output.

Asymptomatic dysfunction is often under-recognized, so a scalable ECG-based screen could help prompt earlier prevention and therapy. However, the evidence is heterogeneous, small-study effects appeared in asymptomatic cohorts, and the authors call for prospective validation before relying on the results.
limitation: The authors report high risk of bias driven by long intervals between ECG and echocardiogram, plus heterogeneity and small-study effects, so the results need cautious interpretation and further prospective validation.
tag: Evidence-backed gain
key_points: For asymptomatic cohorts, pooled AUROC was 0.87 for LVEF ≤35%, 0.93 for LVEF ≤40%, and 0.89 for LVEF ≤50%. | Symptomatic cohorts had pooled AUROCs of 0.89, 0.90, and 0.85 for the same three LVEF thresholds. | Asymptomatic diastolic dysfunction detection was only moderate, with a pooled AUROC of 0.76. | No externally validated symptomatic diastolic dysfunction studies were identified. | Heterogeneity was high (I² above 85%) in the asymptomatic systolic dysfunction analyses.
rundown: The review restricted itself to externally validated AI-ECG studies and searched the literature on 8 September 2025. It treated asymptomatic left ventricular dysfunction as a key stage in the heart failure continuum, and included symptomatic cohorts to give context across that continuum.

For systolic dysfunction, results in asymptomatic groups were described as promising but heterogeneous, and they were similar to those in symptomatic groups. The best pooled result for asymptomatic participants was an AUROC of 0.93 at the LVEF ≤40% threshold.

Diastolic dysfunction performance was lower at 0.76, and the authors noted that no externally validated symptomatic diastolic studies existed for comparison. Small-study effects were detected only in asymptomatic cohorts.
sources:
- peer_reviewed | American Journal of Preventive Cardiology | https://doi.org/10.1016/j.ajpc.2026.101691 | 2026-05-30
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