Fig 1.
Selection of study population.
ECG: electrocardiogram.
Fig 2.
Structure of the neural network in our AI model.
Schematic illustration of the neural network model (A). Schematic illustration of bidirectional LSTM(B). Note that two layers of LSTM which have opposite directions of information transfer with the neurons next to each other are stacked up. LSTM: long short-term memory. N: neuron.
Fig 3.
Conversion of ECG data to 2D matrix.
A representative plot of a single beat at induction I picked up from a 12-lead ECG recording (A). The recorded data consists of voltage plotted against time. A representative 2-dimention matrix converted from the 12-lead ECG recording (B). The matrix has 2 axis of induction axis and time axis. The value at the point indicated with dotted grey line in A converted to an element in the matrix is highlighted with dotted blue line in B. Voltage for each induction was recorded in each 2 ms. ECG: electrocardiogram.
Fig 4.
Diagnostic value of the AI model.
ROC curve (A) and probability of receiving urgent revascularization for patients stratified to each quartile range of the model output using the derivation cohort(B). The results from same analysis using validation cohort are shown in panel C and D. The p values were calculated using Fisher’s exact test. ROC: receiver operating characteristic. AUC: area under curve. CI: confidence interval.
Table 1.
The value of model output for each quartile ranges.
Table 2.
Results with threshold giving the best accuracy for validation cohort.