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Objective: This study aims to develop an artificial intelligence‐based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone. Methods: Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12‐lead ECG and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG‐TTE pairs from a single individual, only the earliest data pair was included. All the ECG‐TTE pairs were randomly divided into the training, validation, or testing data set in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: We retrospectively enrolled a total of 26 786 ECG‐TTE pairs and randomly divided them into training (n = 21 732), validation (n = 2 530), and testing data set (n = 2 530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%. Conclusion: Our results demonstrate that a well‐trained CNN algorithm may be used as a low‐cost and noninvasive method to identify patients with left ventricular dysfunction. [ABSTRACT FROM AUTHOR]

Understanding the factors affecting activities of daily living (ADL) is important in Alzheimer's disease (AD), because decline in ADL contributes to many poor health outcomes. Existing studies often investigate the factors in isolation without a theoretical framework. The purpose of the present study was to provide preliminary results on how cognition, physical performance, and behavioral and psychological symptoms of dementia mediate the relationship of aerobic fitness and ADL in AD. A cross‐sectional analysis was used (n = 28: average age 78 [8] years, education 16 (3) years, Mini‐Mental State Examination scores 20 [4]). The results showed that aerobic fitness is not linked to ADL directly, and its association with ADL was mediated by physical performance and global cognition. Our findings provide preliminary support for aerobic fitness as a potential therapeutic target, as enhanced aerobic fitness could simultaneously modify other factors affecting ADL. Nurses are in a unique position for coordinating exercise safety assessment and prescription and educating older adults with AD about exercise participation. [ABSTRACT FROM AUTHOR]