Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG
Heart failure (HF) is a significant healthcare burden worldwide, with an estimated 64.3 million people living with HF [1,2]. Despite advances in treatment, HF remains as a high risk of morbidity and mortality and is the most common diagnosis in hospitalized patients aged over 65 years, with a 5-year survival rate of only 57% [3,4,5]. In the United States, HF affects ~$30.7 billion total annual costs and projection suggests that by 2030, the total cost of HF will increase by 127%, to $69.8 billion [3,6].
Patients suffering with HF with reduced ejection fraction (HFrEF) become less active, leading to repeated hospitalization, resulting in a poor quality of life, including a high medical cost burden . Despite its poor prognosis and high economic burden, HFrEF awareness remains relatively low due to its insidious onset, varied presentation, and syndromic nature . Early diagnosis and timely intervention may prevent irreversible HFrEF progression and mortality . Electrocardiography (ECG) is a low-cost test frequently performed for a variety of purposes, especially basic examination and screening for cardiovascular disease . We developed an artificial intelligence (AI)-enabled ECG algorithm, which can increase the diagnosis of HFrEF [11,12]. However, it is also inconvenient to visit the hospital for a 12-lead ECG.