The authors note several limitations and further research before their work reaches clinics. The population studied may have higher prevalence of atrial fibrillation compared to the general population. The AI has therefore been trained to retrospectively classify clinically indicated ECGs more than for prediction in healthy patients, or those with unexplained stroke, and may need calibration before widespread application to screening of a broader, healthy population.
Patients were considered negative for atrial fibrillation if they did not have verified diagnosis, but there were likely some patients who had been undiagnosed and labelled erroneously, so the AI may have identified what previous testing had not. On the other hand, some of the false-positive patients identified by the AI as having a history of atrial fibrillation (despite being classified as negative by a human) might actually have had undiagnosed atrial fibrillation. Since the AI is only as good as the data it is trained against there could be errors in the interpretation when the test is applied to other populations, such as a individuals without an indicated ECG.
In a linked Comment, Dr Jeroen Hendriks of the University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia, says: "In summary, Attia and colleagues are to be congratulated for their innovative approach and the thorough development and local validation of the AI-enabled ECG. Given that AI algorithms have recently reached cardiologist level in diagnostic performance this AI-ECG interpretation is ground-breaking in creating an algorithm to reveal the likelihood of atrial fibrillation in ECGs showing sinus rhythm."
This study was funded by internal Mayo Clinic resources. It was conducted by researchers from the Mayo Clinic.
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