Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.
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ABSTRACT: 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 predictive 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.
SUBMITTER: Attia ZI
PROVIDER: S-EPMC8327278 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC8500024 | biostudies-literature
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| S-EPMC8890098 | biostudies-literature
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| S-EPMC10232245 | biostudies-literature
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| S-EPMC8719367 | biostudies-literature
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| S-EPMC8245143 | biostudies-literature
Project description:ObjectiveTo validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort.MethodsWe identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment.ResultsFor detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004).ConclusionArtificial intelligence-augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.
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| S-EPMC9708023 | biostudies-literature
Project description:BackgroundPost-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown.AimsThe aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant.MethodsA retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation.ResultsThe performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction.ConclusionsA positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.
| S-EPMC10077316 | biostudies-literature
Project description:AimsA majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria.Methods and resultsAn AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)].ConclusionThe present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
| S-EPMC10944682 | biostudies-literature
Project description:BackgroundArtificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction.ObjectiveThe study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction.MethodsThe study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction.ResultsThe area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set.ConclusionThe deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.
| S-EPMC9751170 | biostudies-literature