DeepCAD Risk Model (AI-based CAD mortality)

DeepCAD Risk Model (AI-based CAD mortality) — 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, and systolic blood pressure) into a simple logistic risk function to estimate near-term mortality probability. The directions of effects used (for example, higher age, higher creatinine, presence of diabetes, prior MI, and active smoking increasing risk; higher LVEF decreasing risk) reflect consistent findings in CAD prognostic literature.

This implementation is NOT a faithful reproduction of any specific published DeepCAD model weights because the original model coefficients (from the named "DeepCAD" publications) were not available in an accessible form for direct replication. If you obtain the original model's parameters or an open-source checkpoint, replace the coefficients in the PHP processing block with those exact values to produce an authentic replication of the original model. Why this matters clinically: AI-derived risk models that integrate imaging, clinical and laboratory data can improve risk stratification compared with single-modality scores.

However, AI models require external validation, calibration, and impact assessment before clinical deployment. Use this calculator only for educational or investigational purposes until validated by external cohorts and local regulatory/ethical review.