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Mayo Clinic researchers developed and tested an artificial intelligence (AI) model that used results from a standard 24-hour urine test to predict kidney stone composition more accurately than a conventional urine-based calculation. Using the AI model, researchers correctly predicted kidney stone composition in 73% of patients, compared with 33% using the conventional calculation alone.

The findings, published in Mayo Clinic Proceedings, could provide doctors and other health care professionals with another source of clinical information when a kidney stone is not available for testing. Knowing what a stone is made of can help guide treatment and prevention strategies. The model is intended to supplement, not replace, existing test results and trends.

"Many patients never have a kidney stone available for analysis, so doctors are often working with an incomplete picture," says Patrick Day, senior author of the study and a principal developer in Mayo Clinic's Department of Laboratory Medicine and Pathology. "Our findings show that AI may help us get more clinically useful information from a test that is already part of kidney stone care. For patients, that could mean more informed treatment decisions. For laboratories and health care systems, it highlights how existing laboratory test data can be used in new ways to support clinical care."

The study's first author, Eric B. , a senior data science analyst in Mayo Clinic's Department of Laboratory Medicine and Pathology, developed the AI model using data from more than 13,000 patients, including age, sex and results from 24-hour urine tests.

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