Classification of Kidney Stone Composition Using an AI Model


This month’s “Virtual Lecture” discusses how artificial intelligence can be utilized to classify kidney stones while demonstrating improvement in quality and patient safety. 


Length of video: 53 minutes


Rickey Carter, Ph.D.

Professor of Biostatistics
Department of Quantitative Health Services
Mayo Clinic, Jacksonville, Florida

Patrick Day, MT(ASCP)

Instructor in Laboratory Medicine and Pathology
Department of Laboratory Medicine and Pathology
Mayo Clinic, Rochester, Minnesota

Paul Jannetto, Ph.D.

Associate Professor of Laboratory Medicine and Pathology
Division of Clinical Biochemistry
Mayo Clinic, Rochester, Minnesota

Learning objectives

Upon completion of this 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.

Intended audience

This program is appropriate for clinicians, pathologists, medical technologists, nurses, pharmacists, and other allied health staff.


The following types of credit are offered for this event:


Mayo Clinic Laboratories is approved as a provider of continuing education programs in the Clinical Laboratory Sciences by the ASCLS P.A.C.E.® program. This program has been approved for a maximum of 1.0 P.A.C.E.® contact hour.

State of Florida

Mayo Clinic Laboratories is approved as a Continuing Education Accrediting Agency for the Clinical Laboratory Sciences for the State of Florida. Florida Board of Clinical Laboratory Personnel has designated this program for General (Clinical Chemistry/UA/Toxicology) credit. This program has been approved for 1.0 contact hour.

To obtain credit

1. Watch the video.

2. Complete the post-test and evaluation that launches immediately following the video.

3. Generate and print your certificate(s).

Credit for this program expires on 6/28/2025.

Level of instruction for this program is intermediate.

Faculty disclosure

Course director(s), planning committee, faculty, and all others who are in a position to control the content of this educational activity are required to disclose all relevant financial relationships with any commercial interest related to the subject matter of the educational activity. Safeguards against commercial bias have been put in place. Faculty members also will disclose any off-label and/or investigational use of pharmaceuticals or instruments discussed in their presentations. Disclosure of this information will be published in course materials so those participants in the activity may formulate their own judgments regarding the presentations.


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MCL Education

This post was developed by our Education and Technical Publications Team.