Presenter(s): Jae Oh, M.D., Michal Shelly Cohen, Peter Noseworthy, M.D., and Saki Ito, M.D.

Learning Objectives
Upon conclusion of this program, participants should be able to:

• Identify utility of artificial intelligence enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe aortic stenosis (AS)
• Describe related feature of ECG morphology and echocardiographic parameters in the AI-ECG model
• Potential Impact of ECG-AI for Echocardiography in patients with AS

ATTENDANCE / CREDIT
Text the session code (provided only at the session) to 507-200-3010 within 48 hours of the live presentation to record attendance. All learners are encouraged to text attendance regardless of credit needs. This number is only used for receiving text messages related to tracking attendance. Additional tasks to obtain credit may be required based on the specific activity requirements and will be announced accordingly. Swiping your badge will not provide credit; that process is only applicable to meet GME requirements for Residents & Fellows.

TRANSCRIPT
Any credit or attendance awarded from this session will appear on your Transcript.

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