1/31/2023 0 Comments Heart faces on ekg(3) used 100,000 ECG recordings (12 leads), for which the diagnosis was annotated by experts, to train a deep neural network algorithm. In a validation cohort, the algorithm showed excellent performance to detect cardiac rhythm abnormalities and outperformed individual cardiologists (2). (2) used a large dataset of single lead ambulatory ECGs (91,232 recordings from 53,877 patients) to train a deep neural network algorithm. Recent studies suggest that machine learning algorithms have strong potential for automated ECG interpretation. (1) did not discuss the potential of computer-interpreted ECG. However, I was surprised that Cook et al. I also agree that innovation in training, as well as an increase in educational resources may help to improve ECG interpretation and patient safety. I fully agree that physicians at all training levels have deficiencies in ECG interpretation. (1) on the accuracy of physicians’ electrocardiogram (ECG) interpretations. I read with interest the systematic review and meta-analysis by Cook et al.
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