Scalability
In primary care settings and emergency units, there is often a lack of
trained specialists to interpret complex ECGs and make rapid
diagnosis.87 Even the combined accuracy of
practitioners in these settings with current computer interpretations
for AF diagnosis remains insufficient.29 This need is
even greater in less developed countries which contribute about 75% to
the overall cardiovascular mortality.7 As discussed
previously, automated AI-ECG interpretation could enable expert level
diagnoses and streamline clinical workflow in these settings.
A network once trained can be fine-tuned to widespread applications and
smart devices. For instance, we have shown the application of an
algorithm trained using 12 lead ECG to detect serum potassium levels and
applied it to a single lead ECG recorded using a
smartphone.88
To bring these tools to the point-of-care we have incorporated AI-ECG
tools into the electronic health record as an ‘AI-ECG
Dashboard’.8 With the click of a button, clinicians
can have all the ECGs available for that patient analyzed with AI-ECG
probability outputs for various diagnoses. A major advantage of
incorporating AI-ECG algorithms into practice is their ability to keep
learning indefinitely as more information is added. This essentially
creates a self-improving healthcare system.36
The application of DNN models to long duration signals like 24-hour
Holter monitor data to detect paroxysmal AF has shown good preliminary
results.79 This has broad applications for development
of a new generation of real-time analytical tools to detect AF from the
growing number of ambulatory monitoring devices.