Recurrent cryptogenic stroke: A potential role for an artificial intelligence–enabled electrocardiogram?

Figures

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  Figure 1. Four of the patient’s standard 12-lead electrocardiograms (ECGs) recorded at different times: baseline ECG, first abnormal ECG identified by the artificial intelligence–enabled ECG (AI-ECG) algorithm, and ECGs performed at the time of the first and second thromboembolic events. Notice that all ECGs demonstrate sinus rhythm and are quite similar to each other and relatively unremarkable to the naked eye despite having very different predicted probabilities of concomitant atrial fibrillation (reported in red; abnormal is greater than 8.70%).

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  Figure 2. Retrospective artificial intelligence–enabled electrocardiogram (AI-ECG) analysis of the patient’s available ECGs in our electronic medical record over a 24-year period. Our current algorithm5 provides an alert to the provider for an abnormal ECG when a likelihood prediction value of greater than 8.70% (dotted red line) is projected. All abnormal ECGs are designated by a red marker in the figure. In this case, the first abnormal ECG would have been reported over 12 years prior to the patient’s first thromboembolic event. The first and second thromboembolic events are circled in black. The black arrows correspond to the ECGs depicted in Figure 1. The red diamond-shaped marker indicates when atrial flutter was recorded (Figure 3). AF = atrial fibrillation.

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  Figure 3. Follow-up electrocardiogram demonstrating atrial flutter with variable atrioventricular block.