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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 mod
AI-ECG Deep Learning Model for Left Ventricular Dysfunction — Explanation and Clinical Context This calculator implements the clinical screening logic described by Attia et al. (Nature Medicine 2019). The original model is a convolutional neural network trained on paired 12-lead ECG and echocardiogram pairs from tens of thousands of patients. The model was trained to detect reduced systolic function defined as left v
Selected references (evidence used to design index): Davies RH et al., "Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning" (J Cardiovasc Magn Reson). This work shows automated algorithms can improve precision vs manual methods. Alabed S et al., "Quality of reporting in AI cardiac MRI segmentation studies" (Frontiers in Cardiovascular Medicine) — review