Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

AI / informaticsScientific reports· Jun 2026

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

Mohamed Naeem

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AI Evaluation & GovernanceAI / informaticsDiagnosisQ1IF 5.0

New AI model shrinks male-female ECG diagnostic accuracy gap sharply

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

Mohamed Naeem

Scientific reports · Jun 2026

Purpose

This paper introduces DA-GAT-v2, a Demographic-Aware Graph Attention Network designed to simultaneously advance diagnostic accuracy and algorithmic fairness in multi-label cardiac abnormality detection.

Results

AI nearly closes the accuracy gap between male and female patients

More results

Automated 12-lead ECG interpretation has achieved strong diagnostic accuracy through deep learning, yet systematic demographic disparities-particularly between male and female patients-undermine the equitable deployment of these systems in clinical practice.

More results

Evaluated on PTB-XL (21,507 recordings), DA-GAT-v2 achieves macro F1 of 0.8952 and AUROC of 0.9762, surpassing all compared baselines.

Cross-dataset validation on Chapman-Shaoxing (10,646 recordings) confirms generalization with a minimal F1 degradation of 0.0150.

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Conclusion

Fairness evaluation is currently limited to sex and age subgroups owing to the absence of race and ethnicity metadata in both utilized datasets.

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

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