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six-lead ecg data  (AliveCor Inc)

 
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    Structured Review

    AliveCor Inc six-lead ecg data
    Performance of the artificial intelligence model based on various <t>electrocardiogram</t> signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).
    Six Lead Ecg Data, supplied by AliveCor Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/six-lead+ecg+data/6+lead+ecg/pmc12088721-142-5-10
    Average 90 stars, based on 1 article reviews
    six-lead ecg data - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study"

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study

    Journal: European Heart Journal. Digital Health

    doi: 10.1093/ehjdh/ztaf025

    Performance of the artificial intelligence model based on various electrocardiogram signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).
    Figure Legend Snippet: Performance of the artificial intelligence model based on various electrocardiogram signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).

    Techniques Used: Diagnostic Assay, Comparison

    Forest plot for subgroup analysis. The forest plot demonstrating the efficacy of the six-lead artificial intelligence model based on electrocardiogram and its possible different efficacy for predicting left ventricular systolic dysfunction by the prespecified characteristics. The plot highlights that although area under the receiver operating characteristic curve values are generally consistent across subgroups, the presence of heart failure significantly affects the model’s performance.
    Figure Legend Snippet: Forest plot for subgroup analysis. The forest plot demonstrating the efficacy of the six-lead artificial intelligence model based on electrocardiogram and its possible different efficacy for predicting left ventricular systolic dysfunction by the prespecified characteristics. The plot highlights that although area under the receiver operating characteristic curve values are generally consistent across subgroups, the presence of heart failure significantly affects the model’s performance.

    Techniques Used:

    Related Articles

    Diagnostic Assay:

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study
    Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

    Comparison:

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study
    Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

    Sampling:

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study
    Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).



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    Performance of the artificial intelligence model based on various <t>electrocardiogram</t> signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).
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    Overview of study visits, technologies used, and remote data collection procedures.
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    Image Search Results


    Performance of the artificial intelligence model based on various electrocardiogram signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).

    Journal: European Heart Journal. Digital Health

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study

    doi: 10.1093/ehjdh/ztaf025

    Figure Lengend Snippet: Performance of the artificial intelligence model based on various electrocardiogram signals. Receiver operating characteristic curves illustrating the diagnostic performance of the artificial intelligence model based on electrocardiogram signals for detecting left ventricular systolic dysfunction. ( A ) Comparison of the artificial intelligence model based on electrocardiogram signals using the 12-lead, 6-lead, and N-terminal pro-B-type natriuretic peptide. ( B ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤35%. ( C ) Performance of the six-lead artificial intelligence model based on electrocardiogram signal with an LVEF threshold of ≤50%. ( D ) Comparison of the six-lead artificial intelligence model based on electrocardiogram signal performance across different LVEF thresholds (≤35%, ≤40%, and ≤50%).

    Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

    Techniques: Diagnostic Assay, Comparison

    Forest plot for subgroup analysis. The forest plot demonstrating the efficacy of the six-lead artificial intelligence model based on electrocardiogram and its possible different efficacy for predicting left ventricular systolic dysfunction by the prespecified characteristics. The plot highlights that although area under the receiver operating characteristic curve values are generally consistent across subgroups, the presence of heart failure significantly affects the model’s performance.

    Journal: European Heart Journal. Digital Health

    Article Title: Artificial intelligence-enhanced six-lead portable electrocardiogram device for detecting left ventricular systolic dysfunction: a prospective single-centre cohort study

    doi: 10.1093/ehjdh/ztaf025

    Figure Lengend Snippet: Forest plot for subgroup analysis. The forest plot demonstrating the efficacy of the six-lead artificial intelligence model based on electrocardiogram and its possible different efficacy for predicting left ventricular systolic dysfunction by the prespecified characteristics. The plot highlights that although area under the receiver operating characteristic curve values are generally consistent across subgroups, the presence of heart failure significantly affects the model’s performance.

    Article Snippet: The AI-ECG model with the six-lead ECG data acquired from AliveCor KardiaMobile 6L achieved an AUROC of 0.924 (95% CI 0.903–0.944) ( ).

    Techniques:

    Overview of study visits, technologies used, and remote data collection procedures.

    Journal: NPJ Digital Medicine

    Article Title: Pandemic-proof recruitment and engagement in a fully decentralized trial in atrial fibrillation patients (DeTAP)

    doi: 10.1038/s41746-022-00622-9

    Figure Lengend Snippet: Overview of study visits, technologies used, and remote data collection procedures.

    Article Snippet: Data collection included self-administered surveys and remote physiologic monitoring through connected devices including six-lead ECG data through the AliveCor KardiaMobile 6 L device and BP data through the Omron HEM-9210T/9200 T Bluetooth blood pressure monitor, all transmitted via the DeTAP mobile application.

    Techniques:

    SCCR Stanford Center for Clinical Research, OAC oral anticoagulant, EKG electrocardiogram, BP blood pressure, HR heart rate, DeTAP decentralized trial in afib patients, App smartphone/mobile application, AF atrial fibrillation, AEs adverse events.

    Journal: NPJ Digital Medicine

    Article Title: Pandemic-proof recruitment and engagement in a fully decentralized trial in atrial fibrillation patients (DeTAP)

    doi: 10.1038/s41746-022-00622-9

    Figure Lengend Snippet: SCCR Stanford Center for Clinical Research, OAC oral anticoagulant, EKG electrocardiogram, BP blood pressure, HR heart rate, DeTAP decentralized trial in afib patients, App smartphone/mobile application, AF atrial fibrillation, AEs adverse events.

    Article Snippet: Data collection included self-administered surveys and remote physiologic monitoring through connected devices including six-lead ECG data through the AliveCor KardiaMobile 6 L device and BP data through the Omron HEM-9210T/9200 T Bluetooth blood pressure monitor, all transmitted via the DeTAP mobile application.

    Techniques: