Prediction of certainty in artificial intelligence-enabled electrocardiography

The 12‑lead ECG provides an excellent substrate for artificial intelligence (AI) enabled prediction of various cardiovascular diseases. However, a measure of prediction certainty is lacking. To assess a novel approach for estimating certainty of AI-ECG predictions. Two convolutional neural networks (CNN) were developed to predict patient age and sex. Model 1 applied a 5 s sliding time-window, allowing multiple CNN predictions. The consistency of the output values, expressed as interquartile range (IQR), was used to estimate prediction certainty. Model 2 was trained on the full 10s ECG signal, resulting in a single CNN point prediction value. Performance was evaluated on an internal test set and externally validated on the PTB-XL dataset. Both CNNs were trained on 269,979 standard 12‑lead ECGs (82,477 patients). Model 1 showed higher accuracy for both age and sex prediction (mean absolute error, MAE 6.9 ± 6.3 years vs. 7.7 ± 6.3 years and AUC 0.946 vs. 0.916, respectively, P