  Early diagnosis of acute coronary artery occlusion based on electrocardiogram
(ECG) findings is essential for prompt delivery of primary percutaneous
coronary intervention. Current ST elevation (STE) criteria are specific but
insensitive. Consequently, it is likely that many patients are missing out on
potentially life-saving treatment. Experts combining non-specific ECG changes
with STE detect ischaemia with higher sensitivity, but at the cost of
specificity. We show that a deep learning model can detect ischaemia caused by
acute coronary artery occlusion with a better balance of sensitivity and
specificity than STE criteria, existing computerised analysers or expert
cardiologists.
