Diagnosis and treatment of new heart failure with reduced ejection fraction by the artificial intelligence–enhanced electrocardiogram

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  Figure 1. AI-ECG dashboard analysis of the patient’s ECGs for multiple pathologies. A: AI-ECG analysis of the patient’s obtained ECGs in our medical system. The algorithm estimates the patient’s age from ECG (or physiologic “AI-ECG age”) interpretation as well as probabilities of various cardiac pathologies (low EF, HCM, and AF). Each AI-evaluated ECG is represented in each pathology box by red dots as “increased likelihood” above our calculated diagnostic threshold and by blue dots for “low likelihood” for each pathology. This patient had 2 ECGs in our system, represented by 2 dots in each AI-ECG box. B: The patient’s most recently recorded ECG. C: The numerical probability values for each pathology screened by the AI-ECG for each ECG in our medical system. The red values correlate with those percentages higher than our diagnostic threshold (“increased likelihood,” also represented in panel A by red dots). In this case, the probability of low EF was exceedingly high (97%–98%) in each of the patient’s AI-ECGs. D: Diastole. E: Systole. Cardiac magnetic resonance imaging confirmed the patient’s significant biventricular HF with reduced EF. AF = atrial fibrillation; AI-ECG = artificial…