1,329 calculators and references across 60 specialties, with formula confidence shown up front.
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
— Explanation and Clinical Context The DeepCAD Risk Model here is presented as a transparent prototype intended to illustrate how an AI-based coronary artery disease (CAD) mortality predictor can be embedded into a clinical website tool. This prototype combines widely reported clinical predictors (age, sex, left ventricular ejection fraction, serum creatinine, diabetes, prior myocardial infarction, active smoking, an
DeepHeart (approximation) — Explanation and Clinical Context DeepHeart in the original publications is a semi-supervised deep learning pipeline trained on large amounts of wearable heart-rate time series and limited labeled clinical data to predict multiple cardiometabolic conditions and to flag abnormal rhythms. The model learns features from continuous heart rate and activity signals rather than relying solely on h
DeepSurv HF Prognostic Model — Explanation and Clinical Context DeepSurv is a Cox-proportional-hazards deep neural network that learns a nonlinear risk function from patient covariates and outputs individualized survival/hazard estimates. Applied to heart failure (HF), DeepSurv-based models can integrate many continuous and interacting predictors (clinical variables, biomarkers, and high-resolution signals) and may i
EHR–AI Heart Failure Onset Predictor — Prototype Explanation and Clinical Context This page implements a transparent, explainable prototype risk-index (0–100) that combines common, routinely recorded EHR variables (age, sex, BMI, systolic BP, heart rate, hypertension, diabetes, coronary disease, atrial fibrillation, renal function, smoking and optional natriuretic peptide level). The index is computed as a weighted s
ESC–AI HF Readmission Prediction Tool: Explanation and clinical context This page implements a transparent prototype logistic model intended to estimate the probability of 30-day all-cause hospital readmission after an index heart failure admission, using routinely available discharge features (age, sex, prior admissions, length of stay, LVEF, systolic blood pressure, heart rate, BUN, sodium, and BNP when available).
ML-HF Risk Stratifier — Explanation and Clinical Context The ML-HF Risk Stratifier presented here is a prototype web tool that approximates the behavior of a gradient-boosting model (CatBoost) for predicting a composite 30-day outcome after a heart failure (HF) emergency department visit or hospitalization: HF-related ED visit, HF hospital readmission, or all-cause death. The underlying published work (Fine et al., 2