TL;DR
ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients, supports the feasibility of ECG-based longitudinal risk prediction across multiple diseases.
Objective. Cardiovascular disease remains one of the leading causes of morbidity and mortality worldwide, highlighting the need for accurate risk prediction. Atrial fibrillation burden (AFB), premature ventricular contraction burden (PVCB), and T-wave alternans (TWA) have been individually associated with adverse outco...
While ECG-Age provides incremental predictive value beyond sex and CA alone, it does not provide additional predictive value when ECG features are included in the feature set for predicting adverse cardiac outcomes.
The application of AI to ECG analysis represents a promising advancement in personalized cardiovascular risk assessment, but further research is needed to ensure the safety, effectiveness, and equitable clinical integration of these technologies.
This review critically analyzes recent ECG-based ML/DL models for CVD prediction, with particular attention to model performance, dataset characteristics, preprocessing strategies, and the integration of explainable artificial intelligence (XAI).
Cardiovascular disease (CVD) remains the leading cause of death worldwide, and early risk identification outside the clinic remains difficult because conventional diagnostic pathways rely on episodic clinical visits. Wearable devices equipped with photoplethysmography (PPG), single-lead electrocardiography (ECG), and a...
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