Naslov (eng)

Rapid exclusion of COVID infection with the artificial intelligence electrocardiogram

Autor

Attia, Zachi
Dugan, Jennifer
Pereira, Naveen
Noseworthy, Peter A.
Lopez Jimenez, Francisco
Cruz, Jessica
Carter, Rickey E.
DeSimone, Daniel C.
Signorino, John
Halamka, John
Chennaiah Gari, Nikhita R.
Sekhar Madathala, Raja
Platonov, Pyotr G.
Gul, Fahad
Janssens, Stefan P.
Narayan, Sanjiv
Upadhyay, Gaurav A.
Alenghat, Francis J.
Lahiri, Marc K.
Dujardin, Karl
Hermel, Melody
Dominic, Paari
Turk-Adawi, Karam
Asaad, Nidal
Svensson, Anneli
Fernadez-Aviles, Francisco
Esakof, Darryl D.
Bartunek, Jozef
Noheria, Amit
Sridhar, Arun R.
Lanza, Gaetano A.
Cohoon, Kevin
Padmanabhan, Deepak
Alberto Pardo Gutierrez, Jose
Sinagra, Giangranco
Merlo, Marco
Zagari, Domenico
Rodriguez Escenaro, Brenda D.
Pahlajani, Dev B.
Lončar, Goran
Vukomanović, Vladan
Jensen, Henrik K.
Farkouh, Michael E.
Luescher, Thomas F.
Lam Su Ping, Carolyn
Peters, Nicholas S.
Friedman, Paul A.
Kapa, Suraj

Doprinosi

Discover Consortium - Digital and Noninvasive Screening for COVID-19 with AI ECG Repository

Opis (eng)

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative pre- dictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Jezik

engleski

Datum

2021

Licenca

© All rights reserved

Predmet

Ključne reči: COVID, koronavirus, veštačka inteligencija, mašinsko učenje, respiratorne infekcije, elektrokardiogram

Deo kolekcije (1)

o:2793

Radovi saradnika Instituta za kardiovaskularne bolesti "Dedinje"