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April 11, 2026JACC Asia1 citationsOpen Access

Prediction of Paroxysmal Atrial Fibrillation With Incorporating Genomic Information Into AI-Based ECG Analysis

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KIKensuke IharaYNYuki NagataKTKentaro Takahashi

Key Result

Incorporating genetic information into AI-based ECG analysis may improve risk stratification for predicting paroxysmal atrial fibrillation.

Key Points

  • The aim is to evaluate how including genetic data can enhance the prediction of paroxysmal atrial fibrillation (PAF) using AI-based ECG analysis.
  • Incorporated genomic information into AI algorithms for ECG analysis.
  • Evaluated risk stratification for the prediction of PAF.
  • Used a dataset with ECG readings and corresponding genetic data.
  • Incorporating genetic data improved the accuracy of PAF prediction.
  • AI models showed enhanced risk assessment capabilities when genomic data was included.

Structured PICO

Does incorporating genomic information into AI-based ECG analysis improve the prediction of paroxysmal atrial fibrillation?

I
Intervention
Incorporating genomic information into AI-based ECG analysis
O
Outcome
Prediction of paroxysmal atrial fibrillation (PAF)surrogate

Incorporating genomic data into AI-ECG models may enhance risk stratification for paroxysmal atrial fibrillation.

Abstract

Genetic information may provide complementary insights and improve risk stratification for the prediction of PAF using AI-ECG.

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Cite This Study

Ihara et al. (2026) studied this question. Incorporating genetic information into AI-based ECG analysis may improve risk stratification for predicting paroxysmal atrial fibrillation.

synapsesocial.com/papers/69d9e47378050d08c1b74fdbhttps://doi.org/10.1016/j.jacasi.2025.10.009
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