Detecting low ejection fraction from electrocardiogram data using artificial intelligence

A system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable memories storing instructions executable by the one or more processors to cause the system to:
• obtain electrocardiogram (ECG) data for a subject, wherein the ECG data for the subject comprises non-paced ECG data for the subject;
• provide the ECG data to an ensemble of trained artificial intelligence (AI) models, wherein each trained AI model of the ensemble is trained to determine a score of low ejection fraction (EF) based on the ECG data;
• determine a combined score of low EF for the subject based on a weighted combination of the scores of low EF determined by each trained AI model;
• select, from a plurality of operating point thresholds that correspond to combinations of subject characteristics, an operating point threshold for the subject based on a representation of age of the subject and a representation of sex of the subject;
• compare the combined score of low EF to the operating point threshold;
• and
• determine an indication of low EF for the subject based on the comparison.

A non-transitory computer-readable medium storing instructions executable by one or more processors to cause actions to be performed, the actions comprising:

• obtaining electrocardiogram (ECG) data for a subject, wherein obtaining the ECG data for the subject comprises obtaining non-paced ECG data for the subject;
• providing the ECG data to an ensemble of trained artificial intelligence (AI) models, wherein each trained AI model of the ensemble is trained to determine a risk of low ejection fraction (EF) based on the ECG data;
• determining, using one or more fully connected layers, a combined risk of low EF for the subject based on the risks of low EF determined by each trained AI model;
• selecting, from a plurality of operating point thresholds that correspond to combinations of subject characteristics, an operating point threshold for the subject based on a representation of age of the subject and a representation of sex of the subject;
• comparing the combined risk of low EF to the operating point threshold;
• and
• determining an indication of low EF for the subject based on the comparison.