  To drive health innovation that meets the needs of all and democratize
healthcare, there is a need to assess the generalization performance of deep
learning (DL) algorithms across various distribution shifts to ensure that
these algorithms are robust. This retrospective study is, to the best of our
knowledge, the first to develop and assess the generalization performance of a
deep learning (DL) model for AF events detection from long term beat-to-beat
intervals across ethnicities, ages and sexes. The new recurrent DL model,
denoted ArNet2, was developed on a large retrospective dataset of 2,147
patients totaling 51,386 hours of continuous electrocardiogram (ECG). The
models generalization was evaluated on manually annotated test sets from four
centers (USA, Israel, Japan and China) totaling 402 patients. The model was
further validated on a retrospective dataset of 1,730 consecutives Holter
recordings from the Rambam Hospital Holter clinic, Haifa, Israel. The model
outperformed benchmark state-of-the-art models and generalized well across
ethnicities, ages and sexes. Performance was higher for female than male and
young adults (less than 60 years old) and showed some differences across
ethnicities. The main finding explaining these variations was an impairment in
performance in groups with a higher prevalence of atrial flutter (AFL). Our
findings on the relative performance of ArNet2 across groups may have clinical
implications on the choice of the preferred AF examination method to use
relative to the group of interest.
