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Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a deep neural network (DNN) model risk prediction based on ECG would be superior to age and clinical variables at selecting a population at high risk for AF and AF-related stroke. Methods: We retrospectively included all patients with an ECG at Geisinger without a prior history of AF. Incidence of AF and AF-related strokes were identified as outcomes within 1 and 3 years after the ECG, respectively. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. We selected a high-risk cohort for AF screening based on five risk stratification methods - criteria from four clinical trials (mSToPS, STROKESTOP, GUARD-AF and SCREEN-AF) and the DNN model at the qualifying ECG. We simulated patient selection and evaluated outcomes for twenty 1-year periods between 2010-2014 centered around the ECG encounter. For the clinical trials, the patients were considered eligible if they met the criteria before or within the period unless they satisfied exclusion criteria at the time of ECG. Results: The DNN model achieved optimal sensitivity (65%), PPV (10%), NNS for AF (10) within this population compared with all other risk models with a NNS for AF-related stroke of 160. Total screening number, sensitivity, positive predictive value (PPV) and number needed to screen (NNS) to capture AF and AF-related stroke are summarized in Table 1. The number of additional screens for the DNN model was slightly higher for two of the other models (SCREEN-AF and STROKESTOP) but lower than the other two (mSToPS and GUARD-AF). Conclusions: A DNN ECG-based risk prediction model is superior to contemporary AF-screening criteria based on age alone or age and clinical features in selecting a population for additional screening due to high risk for future AF and potential AF-related strokes.