AI used to better predict kidney stone composition
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.
Researchers at Mayo Clinic have developed an artificial intelligence (AI) model that can more accurately predict the composition of kidney stones using data from a standard 24-hour urine test. The AI model correctly identified kidney stone composition in 73% of patients, compared to 33% accuracy using traditional urine-based calculations alone.
The study, published in Mayo Clinic Proceedings, suggests that the AI model could provide doctors with additional clinical information when a kidney stone is not available for testing. This information can help guide treatment and prevention strategies. The AI model is intended to complement existing test results and trends, not replace them.
The model was developed using data from more than 13,000 patients and was validated with data from 1,653 additional patients. Kidney stones are a common issue, affecting one in 11 Americans at some point in their lives, with a high recurrence rate of 60% to 80%. Identifying the type of stone can help doctors determine the best treatment and prevention strategies.
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