Zachi I. Attia
Mayo Clinic
Deep learningQT intervalInternal medicineArtificial intelligenceCardiologyRetrospective cohort studySinus rhythmSudden cardiac deathAsymptomaticHeart failureAtrial fibrillationMEDLINELong QT syndromeElectrocardiographyHyperkalemiaDigital healthPopulationIn patientNon invasiveCorrected qtComputer scienceConfidence intervalClinical trialMedicineCohortArea under the curveEjection fractionConvolutional neural network
Publications 92
#1David M. Harmon (Mayo Clinic)H-Index: 2
#2Daniel R. Witt (Mayo Clinic)H-Index: 2
Last. Zachi I. Attia (Mayo Clinic)H-Index: 12
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#1Konstantinos C. Siontis (Mayo Clinic)H-Index: 23
#2Kan Liu (Mayo Clinic)
Last. Michael J. Ackerman (Mayo Clinic)H-Index: 144
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Abstract null null Background null There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years). null null null Methods null We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) w...
#1Konstantinos C. Siontis (Mayo Clinic)H-Index: 23
#2Peter A. Noseworthy (Mayo Clinic)H-Index: 45
Last. Bernard J. Gersh (Mayo Clinic)H-Index: 163
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#1Michal Cohen-Shelly (Mayo Clinic)H-Index: 1
#2Zachi I. Attia (Mayo Clinic)H-Index: 12
Last. Jae K. Oh (Mayo Clinic)H-Index: 119
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Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography a...
5 CitationsSource
#2Zachi I. AttiaH-Index: 12
Last. Paul A. FriedmanH-Index: 69
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#1Ali Ahmad (Mayo Clinic)H-Index: 5
#2Michel T. Corban (Mayo Clinic)H-Index: 12
Last. Lilach O. Lerman (Mayo Clinic)H-Index: 92
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#1Zachi I. Attia (Mayo Clinic)H-Index: 12
#2Suraj Kapa (Mayo Clinic)H-Index: 30
Last. John Signorino (Mayo Clinic)
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Abstract null null Objective null To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). null null null Methods null 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, ...
#1J. M BosH-Index: 5
#2Matthew SchramH-Index: 2
Last. Michael J. AckermanH-Index: 144
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#1Elaine Y. Wan (Columbia University)H-Index: 18
#2Hamid Ghanbari (UM: University of Michigan)H-Index: 21
Last. David D. McManus (UMMS: University of Massachusetts Medical School)H-Index: 60
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Abstract null null This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, their benefits, shortcomings and areas for technological improvement. Although, wearables for rhythm diagnosis and management has not been examined in large rando...
#1David M. HarmonH-Index: 2
Last. Zachi I. AttiaH-Index: 12
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