AI Insight
This study demonstrates that single-lead ECG configurations used in consumer wearable devices disproportionately reduce AI diagnostic accuracy for elderly patients compared to younger adults. Using over 21,000 ECG recordings, researchers found that reducing from 12-lead to 1-lead ECGs decreased diagnostic accuracy by 14.1 percentage points in patients aged 75 and older, but only 0.4 percentage points in those under 40—a 40-fold difference. The disparity appears linked to greater diagnostic complexity in older patients, who often present with multiple concurrent cardiac conditions.
Why it matters
These findings have significant implications for the rapidly growing market of consumer wearable cardiac monitors, suggesting that simplified ECG devices may be substantially less reliable for elderly users who are already at highest risk for cardiac disease. The results call for age-stratified performance reporting in both clinical validation studies and regulatory approval processes for wearable AI-ECG devices.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Consumer wearable devices increasingly use single-lead electrocardiograms (ECGs) for cardiac monitoring, but these signals contain substantially less spatial information than the clinical 12-lead standard. Whether this reduction dispro- portionately affects older adults, who often present with more complex cardiac conditions, remains poorly understood. In this study, we evaluated the impact of lead reduction on AI-ECG diagnostic performance across age groups. A 1D resid- ual neural network was trained on 21,091 PTB-XL ECG recordings spanning five diagnostic superclasses and assessed using 12-, 6-, 2-, and 1-lead configurations. Under the full 12-lead setting, model accuracy declined from 84.5% in patients younger than 40 years to 66.2% in patients aged 75 years or older. Progressive lead reduction further widened this gap. Under the 1-lead configuration, accuracy decreased by 14.1 percentage points in the 75+ group but by only 0.4 percent- age points in the <40 group, representing an approximately 40-fold differential degradation confirmed by three independent statistical tests (all p < 0.0001). Older adults also exhibited greater multi-condition diagnostic complexity, pro- viding a plausible explanation for their increased vulnerability to information loss. External validation on the MIT-BIH Arrhythmia Database confirmed cross- dataset model stability. These findings suggest that age-stratified performance reporting should be a minimum standard in wearable AI-ECG validation and regulatory assessment.