Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with
a higher risk of important adverse health outcomes such as stroke and death. AF is
linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is
the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not
detect individuals with paroxysmal AF. In this study, we developed machine learning
models for discrimination of prevalent AF using a combination of image-derived radiomics
phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF
in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling
by building sex-specific models. Our integrative model including radiomics and ECG
together resulted in a better performance than ECG alone, particularly in women. ECG
had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding
radiomics features, the accuracy of the model was able to improve significantly. The
sensitivity also increased considerably in women by adding the radiomics (0.68 vs
0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel
insights into AF-related electro-anatomic remodelling and its variations by sex. The
integrative radiomics-ECG model also presents a potential novel approach for earlier
detection of AF.
