Researchers developed, trained, and tested three independent artificial intelligence models to diagnose atherosclerotic cardiovascular diseases.
By Hugo Francisco de SouzaOct 24 2023Reviewed by Danielle Ellis, B.Sc. In a recent study published in eClinicalMedicine, researchers developed, trained, and tested three independent artificial intelligence models to diagnose atherosclerotic cardiovascular disease . Each model was developed to evaluate a separate stage of the ASCVD pathway: elevated coronary artery calcium , obstructive coronary artery disease , and regional left ventricular akinesis.
Conventional, evidence-based ASCVD diagnosis relies on evaluations of a patient's clinical and demographic information. The most popular are pooled cohort equations that use clinical and demographic data to provide a 10-year ASCVD risk assessment. These findings suggest that AI may present untapped value for cardiovascular health assessment without requiring specialized equipment and expensive, invasive clinical procedures.
All models were convolutional neural networks with architecture and training method choices deriving from previous research with successful outcomes. The validation dataset was used for hyperparameter tuning. Model input data comprised a 10-s ECG signal with 12 leads. Each lead contains 5,000 samples .
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