AFNet AI Prediction Model (Paroxysmal AF Detection)

AFNet AI Prediction Model (Paroxysmal AF detection): explanation and context This tool provides two complementary approaches: (1) a CHARGE-AF–style clinical proxy estimator that uses demographic and common clinical predictors to compute an approximate probability of paroxysmal AF detection on prolonged monitoring, and (2) an optional slot to combine an external ECG-AI model probability (if you have a deployed ECG model that returns a numeric probability). The clinical proxy implemented here is intentionally transparent and interpretable; it must be recalibrated to local populations before clinical use. Clinical risk models such as CHARGE-AF were developed to predict incident AF using simple clinical variables and remain widely used as a baseline comparator.

Recent electrocardiogram-based deep learning models (end-to-end ECG AI and convolutional networks) have shown promising performance for predicting current or future AF from ECG traces, but these require model weights and careful external validation before clinical deployment. If you possess the original AFNet / AF detection model weights or an API endpoint that returns a calibrated probability, integrate that into the ECG-AI slot: send the raw ECG trace to your model, receive probability, then replace the ensemble logic here with the published model's exact formula or calibrated stacking.