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Using AI to eliminate mistakes in kidney stone analysis  

Anyone who’s ever had the displeasure of dealing with kidney stones knows firsthand how painful these hard deposits of minerals and salts that form inside the kidneys can be while working their way through the urinary tract.

To effectively treat and prevent kidney stones, it’s important to first understand what the stones are made of. This is typically done manually in the lab using Fourier transform infrared spectroscopy. However, like anything else that’s done manually, mistakes can happen. And these mistakes in manual kidney stone interpretation and reporting can sometimes result in patients receiving the wrong treatments and prevention strategies.

In an effort to eliminate interpretation mistakes and in turn improve diagnosis and treatment options for patients who have kidney stones, a team of researchers from Mayo Clinic’s Department of Laboratory Medicine and Pathology recently conducted a 12-month study to determine whether the use of artificial intelligence-trained algorithms can find errors in manually reviewed and reported kidney stone composition results.

Read more about the study on

Cory Pedersen

Cory Pedersen is a senior marketing specialist for Mayo Clinic Laboratories.