Title: RokkStar & SuperStar: Using AI/ML to improve the diagnosis and treatment of kidney stones

Presenter: Jeff Meeusen, Ph.D. and Patrick Day, MT(ASCP)

Learning Objectives
Upon completion of this activity, participants should be able to:

• Recognize the clinical utility of kidney stone composition analysis and the challenges of the standard clinical workflow. 
• Describe the benefits of using AI-augmented clinical laboratory tests for kidney stone composition analysis. 
• Recognize the clinical utility of the 24-hour urine supersaturation test and the challenges of interpreting this laboratory panel. 

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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