Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.
An AI model designed for children's heart disease can detect dangerous heart changes from simple single-lead ECGs, enabling remote monitoring worldwide.
This multicenter study presents the first AI model specifically adapted for noise and artifact patterns in pediatric/CHD single-lead ECGs, addressing a major unmet need given the lifelong monitoring burden of CHD patients and disparities in access to echocardiography globally. Validation across Boston, Philadelphia, and Toronto centers demonstrates geographic and demographic generalizability, supporting potential deployment in smartwatch-based remote monitoring for CHD populations.
What the study was
- Study design
- retrospective_multicenter_validation
- Population
- Pediatric and congenital heart disease patients
- Sample size
- ~113,494
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- NPJ Digital Medicine
Why it surfaced
Wearable/smartwatch AI for cardiac screening in high-risk populations is a near-term deployment target; pediatric/CHD ECG AI is underserved vs adult population models; large multicenter validation dataset (113k+ patients) provides strong evidence base; NPJ Digital Medicine publication signals high clinical impact perception.
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