Beyond Prediction: The Kai Digital Twin Score Revolutionizes Cardiac Risk Assessment Through In-Silico Modeling and Synthetic Data

Status: In Publication / Submitted

Authors: Zain Khalpey, Ujjawal Kumar, Nicholas King, Amina H. Khalpey

Abstract

This paper introduces the Kai Digital Twin Score, a novel framework for cardiac risk assessment that leverages in-silico modeling and synthetic data generation. Moving beyond traditional predictive models, the Kai Score creates patient-specific digital twins that simulate cardiac physiology and predict post-operative outcomes with enhanced granularity.

The framework integrates multi-modal patient data — including hemodynamic parameters, imaging features, and biomarker profiles — to construct high-fidelity digital representations of individual cardiac patients. Synthetic data augmentation addresses the persistent challenge of limited training datasets in surgical populations, enabling more robust model development without compromising patient privacy.

Preliminary validation demonstrates significant improvements in risk stratification accuracy compared to established scoring systems such as STS and EuroSCORE II, particularly in identifying high-risk subpopulations that traditional models frequently misclassify.