Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes.

ABSTRACT: 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, an original attempt to develop and assess the generalization performance of a DL model for AF events detection from long term beat-to-beat intervals across geography, ages and sexes. The new recurrent DL model, denoted ArNet2, is developed on a large retrospective dataset of 2,147 patients totaling 51,386 h obtained from continuous electrocardiogram (ECG). The model's generalization is evaluated on manually annotated test sets from four centers (USA, Israel, Japan and China) totaling

SUBMITTER: Biton S 

PROVIDER: S-EPMC10023682 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature