Abstract
1 min readIntroduction: This study aimed to assess the ability of a deep learning algorithm, Reti-AF, developed from retinal photos, to predict atrial fibrillation (AF) incidence. Its predictive performance was evaluated using the UK Biobank and compared to Reti-CAC, a similar algorithm trained on coronary artery calcium (CAC) scores. Hypothesis: We hypothesised that Reti-AF and Reti-CAC could potentially predict the incidence of AF. Methods: Due to the lack of atrial fibrillation (AF) patients in the UK Biobank, a Masked AutoEncoder method was used with a Vision Transformer (ViT) model. The model was first trained on 636,992 fundus images from a health screening dataset, then fine-tuned with an additional 6,068 retinal images from the UK Biobank. The survival analysis of the UK Biobank's longitudinal data was performed to assess the model's predictive performance for AF, with hazard ratios calculated. Results: In longitudinal validation among participants without baseline AF, Reti-AF was significantly associated with AF incidence (HR=5.50, 95% CI, 1.98-2.57, highest tertile of Reti-AF versus the first tertile) in a univariable model. However, this significance was not maintained in a multivariable model (HR=1.23, 95% CI, 0.93-1.30). When Reti-CAC was applied to predict AF, it showed a significant association with AF incidence in both univariable (HR=9.30, 95% CI, 6.68-12.96, highest tertile of Reti-CAC versus the first tertile) and multivariable (HR=1.84, 95% CI, 1.26-2.70) models after adjustment for age, gender, smoking, diabetes, hyperlipidemia, hypertension, and baseline chronic kidney disease. Conclusions: The study found that combining deep learning with retinal photos and coronary artery calcium (CAC) as a reference, led to more accurate prediction of atrial fibrillation (AF) incidence compared to using AF. Furthermore, Reti-CAC was shown to be capable of predicting both cardiovascular disease onset and AF incidence.
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