International journal of cardiology
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To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. ⋯ The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
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The impact of fibrinolysis-first strategy on outcomes of patients with ST-segment-elevation myocardial infarction (STEMI) during the COVID-19 pandemic was unknown. ⋯ The fibrinolysis-first strategy during the COVID-19 pandemic was associated with a lower rate of timely coronary reperfusion and increased rates of recurrent ischaemia, cardiogenic shock, and exacerbated heart failure. However, the in-hospital NACE remained similar to that in 2019.
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Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practice owing to their lack of interpretability. We developed an explainable DLM to detect AF using ECG and validated its performance using diverse formats of ECG. ⋯ Our DLM successfully detected AF using diverse ECGs and described the reason for this decision. The results indicated that an explainable artificial intelligence methodology could be adopted to the DLM using ECG and enhance the transparency of the DLM for its application in clinical practice.