Deep Learning Detects LV Diastolic Dysfunction from Standard ECGs

ArticleMoon-Seung SohResearch

A research team led by Moon-Seung Soh developed a vision transformer model to identify left ventricular diastolic dysfunction (LVDD) from standard 12-lead ECGs. With a high negative predictive value, the model shows potential as a scalable, non-invasive initial screening tool.

Key points

  1. Retrospective study using paired ECG and echocardiography records from 21,385 patients at Ajou University School of Medicine.
  2. A vision transformer pretrained on unlabelled ECGs was fine-tuned according to 2016 ASE/EACVI diagnostic criteria.
  3. The test set (15.3% prevalence) demonstrated an AUROC of 0.913, with 83.8% sensitivity and 82.5% specificity at a balanced threshold.
  4. The negative predictive value reached 96.6%, while the positive predictive value was 46.3%.
  5. Because the findings come from a retrospective single-centre study, prospective external validation is necessary before clinical deployment.

Why it matters

LVDD is an established precursor to heart failure with preserved ejection fraction (HFpEF), but diagnosing it requires multiple Doppler echocardiography parameters, making population-level screening impractical. Using routine 12-lead ECGs could help triage patients for comprehensive echocardiography before symptomatic HFpEF develops.

Analysis

Due to its high negative predictive value (96.6%), the model operates primarily as a rule-out screening tool in clinical settings. This could significantly streamline echocardiography referrals. However, external prospective trials are critical to confirm whether the model maintains performance outside the single-centre training environment.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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