Detecting Atrial Fibrillation by Artificial Intelligence-Enabled Neuroimaging Examination

Abstract

Introduction: Diagnosis of occult atrial fibrillation (AF) is difficult as it is often asymptomatic, leading to under-detection. Current diagnostic tests have variable limitations in feasibility and accuracy. Machine learning is gaining greater traction for clinical decision-making and may help facilitate the detection of undiagnosed AF when applied to magnetic resonance imaging (MRI). We hypothesize that a machine learning algorithm increases the accurate classification of MRIs of stroke patients into those due to AF versus large artery atherosclerosis. Methods: Stroke aetiology for each patient was determined by a review of medical records and investigations. Patients with either AF or lar..