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Background: Electrocardiogram (ECG) interpretation is an integral part of the clinical ECG workflow; however, this process is often time‐consuming and labor‐intensive. We aim to develop a rapid, inexpensive means to detect abnormal ECGs using artificial intelligence (AI) from scanned ECG printouts. Methods: The study included 1172 12‐lead ECG scans performed in 1172 individuals from a community in Guangzhou, China; 878 (74.9%) were diagnosed with sinus rhythm, and the remaining 294 (25.1%) with abnormal rhythms. A deep learning model consisting of a convolutional neural network based on InceptionV3 and a fully connected layer followed by a GEV activation was trained to classify scanned tracings as either normal or abnormal. Results: In a hold‐out testing set, the model achieved a area under curve (AUC), sensitivity, specificity, PPV, and NPV of 0.932 (95% confidence interval [CI]: 0.890, 0.976), 0.816 (95% CI: 0.657, 0.923), 0.993 (95% CI: 0.959, 1.0), 0.969 (95% CI: 0.838, 0.999), and 0.950 (95% CI: 0.90, 0.980) respectively, when using a probability threshold of 0.5. When compared with a physiological expert, these results show comparable performance with a statistically significant increase in specificity and a non‐significant decrease in sensitivity at the 95% level. Conclusions: We have developed a rapid, inexpensive, accurate means to detect abnormal ECGs using AI. Easy and accurate identification of such "abnormal" ECGs could allow the mass automated review of ECGs in community settings where abnormal ones could be flagged using AI for detailed clinical review by healthcare professionals. [ABSTRACT FROM AUTHOR]

Background: Increased professional commitment is essential for relieving the nursing workforce shortage, which is exacerbated by the COVID-19 pandemic. The professional commitment of graduating nursing students is a powerful predictor of their work commitment. However, limited information is available regarding graduating nursing students' professional commitment. Existing studies investigating nursing students' professional commitment are limited by their lack of theoretical foundation., Objectives: To investigate the level of graduating nursing students' professional commitment and its multilevel influencing factors from the perspective of the Ecological Systems Theory in the early days following the COVID-19 outbreak., Design: A descriptive cross-sectional study., Setting: Three educational institutions in Shanghai, China., Participants: 513 nursing students who were graduating with an associate or bachelor's degree., Methods: The independent variables were measured by a self-designed questionnaire. The dependent variable professional commitment was measured by the Nursing Professional Commitment Scale. Hierarchical regression analyses, which allowed the independent variables entered in order, were performed to identify the significant predictor variables of the professional commitment and its dimensions., Results: The level of professional commitment was 100.15 ± 20.35 (score ranged between 34 and 136). The individual factors (degree, whether had received a scholarship during the past academic years, ΔR 2  = 0.142), family factors (parents and siblings' attitudes towards one's majoring in nursing, ΔR 2  = 0.153), educational factors (academic faculty's belief in nursing profession, leaders' emphasis on nursing profession, satisfaction with clinical instructors' role modeling, ΔR 2  = 0.097), and social factors (reason for majoring in nursing, perceived nurse-patient relationship, ΔR 2  = 0.153) were significant predictors of the graduating nursing students' professional commitment (R 2  = 47.6 %, F = 32.277, p < 0.001)., Conclusions: The graduating nursing students had a moderate level of professional commitment, which needed to be improved. Nursing educators should comprehensively consider various factors to understand the relationship between individuals and environmental systems, and implement targeted interventions to shape the positive professional values of nursing students., Competing Interests: Declaration of competing interest None., (Copyright © 2022 Elsevier Ltd. All rights reserved.)