An ECG records the electrical activity of the heart. Its pattern changes in cardiac conditions including heart attacks and atrial fibrillation.

The team trained two versions of the AI: in one, the algorithm was only given the raw ECG data, which measures voltage over time. In the other, it was fed ECG data in combination with patient age and sex.

They measured the AI’s performance using a metric known as AUC, which measures how well a model distinguishes between two groups of people – in this case, patients who died within a year and those who survived. The AI consistently scored above 0.85, where a perfect score is 1 and a score of 0.5 indicates no distinction between the two groups.

The AUCs for risk scoring models currently used by doctors range between 0.65 and 0.8, says Fornwalt.

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