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.