o:2966 Rapid exclusion of COVID infection with the artificial intelligence electrocardiogram en 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. Ključne reči: COVID, koronavirus, veštačka inteligencija, mašinsko učenje, respiratorne infekcije, elektrokardiogram 91552100 125801225 1552101 1942-5546 1552099 10.1016/j.mayocp.2021.05.027 2023-10-13T10:10:14.565Z 44 yes 46 Zachi Attia 105100553 2021 46 Suraj Kapa 46 Jennifer Dugan 46 Naveen Pereira 46 Peter A. Noseworthy 46 Francisco Lopez Jimenez 46 Jessica Cruz 46 Rickey E. Carter 46 Daniel C. DeSimone 46 John Signorino 46 John Halamka 46 Nikhita R. Chennaiah Gari 46 Raja Sekhar Madathala 46 Pyotr G. Platonov 46 Fahad Gul 46 Stefan P. Janssens 46 Sanjiv Narayan 46 Gaurav A. Upadhyay 46 Francis J. Alenghat 46 Marc K. Lahiri 46 Karl Dujardin 46 Melody Hermel 46 Paari Dominic 46 Karam Turk-Adawi 46 Nidal Asaad 46 Anneli Svensson 46 Francisco Fernadez-Aviles 46 Darryl D. Esakof 46 Jozef Bartunek 46 Amit Noheria 46 Arun R. Sridhar 46 Gaetano A. Lanza 46 Kevin Cohoon 46 Deepak Padmanabhan 46 Jose Alberto Pardo Gutierrez 46 Giangranco Sinagra 46 Marco Merlo 46 Domenico Zagari 46 Brenda D. Rodriguez Escenaro 46 Dev B. Pahlajani 46 Goran Lončar 29116519 0000-0001-8553-683X 46 Vladan Vukomanović 61903369 0000-0001-7197-5794 46 Henrik K. Jensen 46 Michael E. Farkouh 46 Thomas F. Luescher 46 Carolyn Lam Su Ping 46 Nicholas S. Peters 46 Paul A. Friedman 46 Discover Consortium - Digital and Noninvasive Screening for COVID-19 with AI ECG Repository institution application/pdf 1183438 https://unilib.phaidrabg.rs/o:2966 no yes 1 70 616.12-073.7:004.85 92000001 71A11 Mayo Clinic Proceedings 96 8 2081 2094 2021