Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Patients with multiple myeloma (MM) are at a high risk for developing cardiovascular complications. Global longitudinal strain (GLS) can detect early functional impairment before structural abnormalities develop. It remains unknown if reduced GLS is associated with reduced survival in patients with MM. We conducted a retrospective cohort analysis of patients diagnosed with MM between 1 January 2000 and 31 December 2017 at our institution. Patients with a 2D transthoracic echocardiogram completed within 1 year of MM diagnosis, left ventricular ejection fraction (LVEF) greater than 40%, and no history of myocardial infarction prior to MM diagnosis were included. GLS was measured using an artificial-intelligence-powered software (EchoGo Core), with reduced GLS defined as an absolute value of 9.6, LVEF/GLS > 4.1, higher LV mass index, and low-voltage ECG. A Total of 126 (52.1%) deaths occurred during follow-up. Overall survival was lower among patients with reduced GLS (adjusted HR: 1.81, CI: 1.07–3.05).