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Atrial fibrillation (AF) is a progressive disease involving both structural and functional remodeling. To investigate the contribution of cell-scale functional remodeling to AF pathogenesis, we combined personalized 3D anatomical models with pathology-specific ionic models. The latter were developed using recordings in myocytes isolated from patients in sinus rhythm, paroxysmal, postoperative, and persistent AF. To quantify AF dynamics, we developed a novel algorithm for locating RDs by backtracking the conduction velocity field from the wavebreak regions. We demonstrate that our novel algorithm is at least 700 times faster than the traditional phase singularity analysis. The inducibility of simulated AF was not pathology-dependent, but pathological models demonstrate a more extensive arrhythmogenic substrate compared to the sinus rhythm. AF driver locations depend on electrophysiological remodeling; differences between pathology-specific models are explained by differences in wavebreak patterns. Specifically, RDs tend to dwell in the regions with the highest wavebreak probability.