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  • 1
    Online Resource
    Online Resource
    A deep learning algorithm for detecting acute myocardial infarction
    Europa Digital & Publishing ; 2021
    In:  EuroIntervention Vol. 17, No. 9 ( 2021-10), p. 765-773
    Details
    In: EuroIntervention, Europa Digital & Publishing, Vol. 17, No. 9 ( 2021-10), p. 765-773
    Type of Medium: Online Resource
    ISSN: 1774-024X
    URL: Article
    DOI: 10.4244/EIJ-D-20-01155
    Language: Unknown
    Publisher: Europa Digital & Publishing
    Publication Date: 2021
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  • 2
    Online Resource
    Online Resource
    Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction
    Springer Science and Business Media LLC ; 2022
    In:  npj Digital Medicine Vol. 5, No. 1 ( 2022-01-19)
    Details
    In: npj Digital Medicine, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2022-01-19)
    Abstract: Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K + and serum laboratory potassium measurement (Lab-K + ) within 1 h were included. ECG-K + had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K + to predict moderate-to-severe hypokalemia (Lab-K + ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K + ≥ 6 mmol/L) were 〉 0.85 and 〉 0.95, respectively. The U-shaped relationships between K + concentration and adverse outcomes were more prominent for ECG-K + than for Lab-K + . ECG-K + and Lab-K + hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K + , patients with hypokalemic ECG-K + had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K + but dyskalemic ECG-K + (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K + not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.
    Type of Medium: Online Resource
    ISSN: 2398-6352
    URL: Article
    DOI: 10.1038/s41746-021-00550-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2925182-5
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  • 3
    Online Resource
    Online Resource
    Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value
    Elsevier BV ; 2022
    In:  Clinica Chimica Acta Vol. 536 ( 2022-11), p. 126-134
    Details
    In: Clinica Chimica Acta, Elsevier BV, Vol. 536 ( 2022-11), p. 126-134
    Type of Medium: Online Resource
    ISSN: 0009-8981
    URL: Article
    DOI: 10.1016/j.cca.2022.09.021
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1499920-1
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  • 4
    Online Resource
    Online Resource
    A deep learning-based system capable of detecting pneumothorax via electrocardiogram
    Springer Science and Business Media LLC ; 2022
    In:  European Journal of Trauma and Emergency Surgery Vol. 48, No. 4 ( 2022-08), p. 3317-3326
    Details
    In: European Journal of Trauma and Emergency Surgery, Springer Science and Business Media LLC, Vol. 48, No. 4 ( 2022-08), p. 3317-3326
    Type of Medium: Online Resource
    ISSN: 1863-9933 , 1863-9941
    URL: Article
    DOI: 10.1007/s00068-022-01904-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2276432-X
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  • 5
    Online Resource
    Online Resource
    Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease
    MDPI AG ; 2022
    In:  Journal of Personalized Medicine Vol. 12, No. 2 ( 2022-02-19), p. 315-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 12, No. 2 ( 2022-02-19), p. 315-
    Abstract: Background: Left atrium enlargement (LAE) can be used as a predictor of future cardiovascular diseases, including hypertension (HTN) and atrial fibrillation (Afib). Typical electrocardiogram (ECG) changes have been reported in patients with LAE. This study developed a deep learning model (DLM)-enabled ECG system to identify patients with LAE. Method: Patients who had ECG records with corresponding echocardiography (ECHO) were included. There were 101,077 ECGs, 20,510 ECGs, 7611 ECGs, and 11,753 ECGs in the development, tuning, internal validation, and external validation sets, respectively. We evaluated the performance of a DLM-enabled ECG for diagnosing LAE and explored the prognostic value of ECG-LAE for new-onset HTN, new-onset stroke (STK), new-onset mitral regurgitation (MR), and new-onset Afib. Results: The DLM-enabled ECG achieved AUCs of 0.8127/0.8176 for diagnosing mild LAE, 0.8587/0.8688 for diagnosing moderate LAE, and 0.8899/0.8990 for diagnosing severe LAE in the internal/external validation sets. Notably, ECG-LAE had higher prognostic value compared to ECHO-LAE, which had C-indices of 0.711/0.714 compared to 0.695/0.692 for new-onset HTN, 0.676/0.688 compared to 0.663/0.677 for new-onset STK, 0.696/0.695 compared to 0.676/0.673 for new-onset MR, and 0.800/0.806 compared to 0.786/0.760 for new-onset Afib in internal/external validation sets, respectively. Conclusions: A DLM-enabled ECG could be considered as a LAE screening tool and provide better prognostic information for related cardiovascular diseases.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm12020315
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662248-8
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  • 6
    Online Resource
    Online Resource
    Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification
    Elsevier BV ; 2023
    In:  Computer Methods and Programs in Biomedicine Vol. 231 ( 2023-04), p. 107359-
    Details
    In: Computer Methods and Programs in Biomedicine, Elsevier BV, Vol. 231 ( 2023-04), p. 107359-
    Type of Medium: Online Resource
    ISSN: 0169-2607
    URL: Article
    DOI: 10.1016/j.cmpb.2023.107359
    RVK:
    XA 37224
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1466281-4
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  • 7
    Online Resource
    Online Resource
    Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study
    MDPI AG ; 2021
    In:  Journal of Personalized Medicine Vol. 11, No. 8 ( 2021-07-27), p. 725-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 11, No. 8 ( 2021-07-27), p. 725-
    Abstract: Background: glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c via ECG. Methods: there were 104,823 ECGs with corresponding HbA1c or fasting glucose which were utilized to train a DLM for calculating ECG-HbA1c. Next, 1539 cases from outpatient departments and health examination centers provided 2190 ECGs for initial validation, and another 3293 cases with their first ECGs were employed to analyze its contributions to DM management. The primary analysis was used to distinguish patients with and without mild to severe DM, and the secondary analysis was to explore the predictive value of ECG-HbA1c for future complications, which included all-cause mortality, new-onset chronic kidney disease (CKD), and new-onset heart failure (HF). Results: we used a gender/age-matching strategy to train a DLM to achieve the best AUCs of 0.8255 with a sensitivity of 71.9% and specificity of 77.7% in a follow-up cohort with correlation of 0.496 and mean absolute errors of 1.230. The stratified analysis shows that DM presented in patients with fewer comorbidities was significantly more likely to be detected by ECG-HbA1c. Patients with higher ECG-HbA1c under the same Lab-HbA1c exhibited worse physical conditions. Of interest, ECG-HbA1c may contribute to the mortality (gender/age adjusted hazard ratio (HR): 1.53, 95% conference interval (CI): 1.08–2.17), new-onset CKD (HR: 1.56, 95% CI: 1.30–1.87), and new-onset HF (HR: 1.51, 95% CI: 1.13–2.01) independently of Lab-HbA1c. An additional impact of ECG-HbA1c on the risk of all-cause mortality (C-index: 0.831 to 0.835, p 〈 0.05), new-onset CKD (C-index: 0.735 to 0.745, p 〈 0.01), and new-onset HF (C-index: 0.793 to 0.796, p 〈 0.05) were observed in full adjustment models. Conclusion: the ECG-HbA1c could be considered as a novel biomarker for screening DM and predicting the progression of DM and its complications.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm11080725
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662248-8
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  • 8
    Online Resource
    Online Resource
    Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases
    SAGE Publications ; 2023
    In:  DIGITAL HEALTH Vol. 9 ( 2023-01)
    Details
    In: DIGITAL HEALTH, SAGE Publications, Vol. 9 ( 2023-01)
    Abstract: The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33–17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82–34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76–17.38), AMI (HR: 4.01, 95% CI: 2.24–7.17), STK (HR: 2.15, 95% CI: 1.70–2.72), and HF (HR: 6.66, 95% CI: 4.54–9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63–9.16) and 2.29 (95% CI: 2.15–2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.
    Type of Medium: Online Resource
    ISSN: 2055-2076 , 2055-2076
    URL: Article
    DOI: 10.1177/20552076231187247
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2819396-9
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  • 9
    Online Resource
    Online Resource
    Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits
    Oxford University Press (OUP) ; 2023
    In:  European Heart Journal - Digital Health Vol. 4, No. 1 ( 2023-01-31), p. 22-32
    Details
    In: European Heart Journal - Digital Health, Oxford University Press (OUP), Vol. 4, No. 1 ( 2023-01-31), p. 22-32
    Abstract: Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits. Methods and results We retrospectively collected 168 450 ECGs with corresponding serum potassium (K+) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K+ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720–0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888–0.915/0.908) in patients with multiple visits. Conclusion Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.
    Type of Medium: Online Resource
    ISSN: 2634-3916
    URL: Article
    DOI: 10.1093/ehjdh/ztac072
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3076078-1
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  • 10
    Online Resource
    Online Resource
    Data_Sheet_1.docx
    Frontiers Media SA ; 2022
    In:  Frontiers in Cardiovascular Medicine Vol. 9 ( 2022-2-8)
    Details
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 9 ( 2022-2-8)
    Abstract: The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs). Methods There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM. Results The mean absolute errors of chronologic age and ECG-age was 6.899 years ( r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age & gt;7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors. Conclusions The ECG-age estimated via DLM provides additional information for CVD incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs compared with chronological age.
    Type of Medium: Online Resource
    ISSN: 2297-055X
    URL: Article
    URL: Article
    DOI: 10.3389/fcvm.2022.754909
    DOI: 10.3389/fcvm.2022.754909.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2781496-8
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