Title: Classification of Kidney Stone Composition using an Artificial Intelligence model: Twelve Month Prospective Study Demonstrates Improvements in Quality and Patient Safety - November 2, 2022
Speakers: Paul Jannetto, Ph.D., Rickey Carter, Ph.D., and Patrick Day, MT(ASCP)

Learning Objectives - Upon conclusion of the activity, participants should be able to:

• Discuss the clinical utility of kidney stone analysis and describe the primary methodology/workflow. 
• Discuss opportunities for computer vision techniques and machine learning in spectra classification tasks
• Describe the benefits of using AI as a laboratory quality tool as it relates to the analysis of kidney stone composition

Audience: DLMP Staff

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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