Perspective:

AF is a common arrhythmia, but may escape diagnosis because it may be intermittent and/or may not be associated with symptoms. In patients at risk for thromboembolism, its diagnosis may call for long-term oral anticoagulation. Therefore, ‘passive’ detection (i.e., that which does not require user intervention) of AF is attractive and can potentially bring at-risk patients to a physician’s attention for management. Although the algorithm performed reasonably well within the ‘clean’ context of patients undergoing cardioversion, its discrimination was lacking in the ambulatory cohort. Despite multiple studies utilizing a host of sensors in the quest to identify AF, the clinician ultimately has to confirm the diagnosis with an electrocardiographic tracing, as opposed to probabilistic results of machine learning, before prescribing therapy with potential hazard (e.g., long-term oral anticoagulation).