Approaches for atrial fibrillation (AF) detection can screen only 1 patient at a time.1 In 2018,2 we demonstrated a novel method of AF detection by analyzing facial photoplethysmographic (FPPG) signals without physical contact using a smartphone camera.2 In this proof-of-concept study, we prospectively evaluated the feasibility of high-throughput AF detection by analyzing FPPG signals3 from multiple patients concurrently using a single digital camera and a pretrained deep convolutional neural network (DCNN).4
Yan BP, Lai WHS, Chan CKY, et al. High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning. JAMA Cardiol. 2020;5(1):105–107. doi:10.1001/jamacardio.2019.4004
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