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- W4386799265 abstract "ObjectiveTo evaluate the ability of a neural network to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using point-of-care electrocardiography obtained with a portable device.Patient and MethodsWe enrolled 2827 patients in a prospective observational study, from December 10, 2020, through June 4, 2021, to determine the accuracy of a point-of-care, handheld, smartphone-compatible, artificial intelligence–enabled electrocardiography (ECG) (POC AI-ECG) in detecting asymptomatic SARS-CoV-2 infection using a modified version of an existing deep learning model framework trained on 12-lead ECG data.ResultsStudy participants were 48% (n=1067) female, 79% (n=1749) White, and 7% (n=153) endorsed previous COVID-19 infection. We found the POC AI-ECG algorithm was ineffective for detecting asymptomatic SARS-CoV-2 infection (area under curve, 0.56; 95% CI, 0.46-0.66), failing to adequately discriminate between ECGs performed among participants who tested positive compared to those who tested negative.ConclusionContrary to the prior 12-lead ECG study, a POC AI-ECG failed to reliably identify asymptomatic SARS-CoV-2 infection among adults. This study underscores the importance of prospective testing, assuring similar populations, and using similar signals or data when developing AI-ECG tools.Trial registrationclinicaltrials.gov Identifier: NCT04725097" @default.
- W4386799265 created "2023-09-17" @default.
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- W4386799265 date "2023-12-01" @default.
- W4386799265 modified "2023-10-06" @default.
- W4386799265 title "RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study" @default.
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- W4386799265 doi "https://doi.org/10.1016/j.mcpdig.2023.07.007" @default.
- W4386799265 hasPublicationYear "2023" @default.
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