Hearts with atrial fibrillation develop structural changes, such as chamber enlargement. Before those changes are visible to standard imaging techniques such as echocardiograms, there is likely fibrosis (scarring) of the heart associated with atrial fibrillation. Additionally, the presence of atrial fibrillation may temporarily modify the electrical properties of the heart muscle, even after it has ended.

The researchers set out to train a neural network -- a class of deep learning AI -- to recognise subtle differences in a standard ECG that are presumed to be due to these changes, although neural networks are "black boxes" and the specific findings that drives their observations are not known. The authors used ECGs of cardiac rhythm acquired from almost 181,000 patients [3] (around 650,000 ECG scans) between December 1993 and July 2017, dividing the data into patients who were either positive or negative for atrial fibrillation.

ECG data was assigned into three groups: training, internal validation and testing datasets with 70% in the training group, 10% in validation and optimisation, and 20% in the testing group (454,789 ECGs from 126,526 patients in the training dataset, 64,340 ECGs from 18,116 patients in the validation dataset and 130,802 ECGs from 36,280 patients in the testing dataset).

The AI performed well at identifying the presence of atrial fibrillation: testing on the first cardiac ECG output from each patient, the accuracy was 79% (for a single scan), and when using multiple ECGs for the same patient the accuracy improved to 83%. Further research is needed to confirm the performance on specific populations, such as patients with unexplained stroke (embolic stroke of undetermined source - ESUS), or heart failure.

The authors of the study also speculate that it may one day be possible to use this technology as a point-of-care diagnostic test in the doctor's surgery to screen high-risk groups. Screening people with hypertension, diabetes, or age over 65 years for atrial fibrillation could help avoid ill health, however, current detection methods are costly and identify few patients. In addition, this screening currently requires wearing a large and uncomfortable heart monitor for days or weeks.

Dr Xiaoxi Yao, a study co-investigator from Mayo Clinic, USA, says: "It is possible that our algorithm could be used on low-cost, widely available technologies, including smartphones, however, this will require more research before widespread application." [2]

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