Electrocardiogram screening for aortic valve stenosis using artificial intelligence

Cohen‐Shelly, Michal; Attia, Zachi I.; Friedman, Paul A.; Ito, Saki; Essayagh, Benjamin; Ko, Wei-Yin; Murphree, Dennis H.; Michelena, Héctor I.; Carter, Rickey E.; Johnson, Patrick W.; Noseworthy, Peter A.; López-Jiménez, Francisco; Oh, Jae K.

doi:10.1093/eurheartj/ehab153

Cited by 137 publications

(107 citation statements)

References 31 publications

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“…However and as stated by the authors, conclusions of this visualization have to be drawn carefully, as it has been shown that class activation mapping can fail to properly localize objects in an image if the image contains multiple occurrences of the same classification (as it is the case in a 12-lead ECG) [ 13 ]. Last, Cohen-Shelly et al showed the feasibility of artificial intelligence based screening for aortic valve stenosis using standard 12-lead ECG [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

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Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Bigler

Seiler

2021

PLoS ONE

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Introduction The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG). Method This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia. Results Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection. Conclusions When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.

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Section: Discussionmentioning

confidence: 99%

“…objects in an image if the image contains multiple occurrences of the same classification (as it is the case in a 12-lead ECG) [13]. Last, Cohen-Shelly et al showed the feasibility of artificial intelligence based screening for aortic valve stenosis using standard 12-lead ECG [39].…”

Section: Plos Onementioning

confidence: 99%

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Bigler

Seiler

2021

PLoS ONE

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“…showed that the TP interval and U waves in the right precordial leads were weighted the most heavily for determining the presence of AS. 2 Typical ECG findings for left ventricular hypertrophy were not weighted in the developed AI. Although those findings and the methodology involved will inspire many researchers, further research is needed to understand the exact meaning of the former.…”

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confidence: 98%

“…developed and validated a DL model for detecting aortic stenosis (AS) using electrocardiography (ECG), and their results are published in this issue of the European Heart Journal . 2 The authors have shown that AI using ECG can identify patients with moderate or severe AS, and might be able to predict developing AS by comparing false-positive and true-negative groups in subgroup analysis. Through learning an implicit representation, the DL model is effective in discovering diverse features based on subtle changes in ECG and creating an algorithm from complex and non-linear ECG data.…”

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confidence: 99%

“…were from Minnesota, Arizona, and Florida, the data were mixed before they were assigned to training and validation data. 2 Absence of external validation might result in an overestimated performance because the training and test data were not distinctly different. Hence, further studies are needed for external validation such that the developed AI model can be applied across regions and hospitals.…”

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confidence: 99%

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Artificial intelligence using electrocardiography: strengths and pitfalls

Kwon

Lee

et al. 2021

European Heart Journal

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Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

Yuan,

Duffy,

Dhruva

et al. 2023

JAMA Cardiol

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ImportanceEarly detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases.ObjectiveTo determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population.Design, Setting, and ParticipantsThis prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm.Main Outcomes and MeasuresA convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center.ResultsA total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater.Conclusions and RelevanceDeep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

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Electrocardiogram screening for aortic valve stenosis using artificial intelligence

Cited by 137 publications

References 31 publications

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

Artificial intelligence using electrocardiography: strengths and pitfalls

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

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