Patient-specific cardiac digital twins that integrate detailed anatomy and electrophysiological function are increasingly important for understanding cardiac arrhythmias and other electrical disorders [1]. While recent advances have enabled automated construction of anatomical models from clinical imaging, a major challenge remains the generation of fully functional cardiac digital twins (CDTs) that reproduce patient-specific electrical behavior directly from non-invasive clinical data, such as the electrocardiogram (ECG). In this work, we present an automated and scalable computational framework for generating anatomical and functional CDTs of the whole heart by integrating medical imaging and full ECG time-series data. Building upon the AugmentA pipeline [2], personalized volumetric cardiac anatomies are generated from segmented CT or MRI data Major anatomical regions relevant for electrophysiological modeling and myocardial fiber architecture are incorporated using a rule-based approach. To enable realistic forward simulations of body-surface potentials, an automated fitting procedure is used to generate subject-specific torso and lung anatomies surrounding the heart. Beyond anatomical reconstruction, we introduce a functional twinning stage in which electrophysiological model parameters are inferred by fitting simulated ECGs to the full clinical ECG sequence employing advanced optimization techniques. This ECG-driven calibration yields patient-specific functional CDTs capable of reproducing both spatial activation patterns and temporal dynamics of cardiac electrophysiology. The framework is evaluated on a cohort of patients, demonstrating robustness, scalability, and the ability to capture inter-subject variability in both anatomy and function. Overall, this approach moves toward fully non-invasive, ECG-based CDTs of the whole heart, with potential applications in personalized diagnosis, treatment planning, and large-scale population studies of cardiac electrical disorders. REFERENCES [1] J. Heijman, et al. Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care. Cardiovascular Research, Vol. 117:1682-1699, 2021. [2] L. Azzolin, et. al. AugmentA: Patient-specific augmented atrial model generation tool. Computerized Medical Imaging and Graphics, Vol. 108, 2023.