Figure 2 . Only the four important classes, discussed in the main text are shown here, the rest was presented in Supplementary material online, Figure S1 . " width="100%" height="100%">
Journal: European Heart Journal. Digital Health
Article Title: Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram
doi: 10.1093/ehjdh/ztab029
Figure Lengend Snippet: Performance of the long short-term memory model and different groups of board-certified doctors in detecting acute ST-elevation myocardial infarction and different heart rhythms. These are the accuracies and receiver operating characteristic curves in detecting ( A ) ST-elevation myocardial infarction ( B ) atrial fibrillation ( C ) complete heart block ( D ) paroxysmal supraventricular tachycardia of our artificial intelligence model and the results of a commercial algorithm and different groups of doctors in the comparative external tests. The orange line was the receiver operating characteristic curve of the long short-term memory model. The different colour points represent different groups of board-certified doctors. AI, artificial intelligence; CV, cardiologists; ER, emergency physicians; LSTM, long short-term memory; MR, internists; abbreviations for the electrocardiogram diagnoses are as in Figure 2 . Only the four important classes, discussed in the main text are shown here, the rest was presented in Supplementary material online, Figure S1 .
Article Snippet: The 12-lead ECG was recorded according to a standardized protocol and lead position at a sampling rate of 500 Hz using a computerized ECG machine (GE Healthcare MAC 2000/3500/5500, USA).
Techniques: Blocking Assay