Kidney stones

Determine stone composition, risk factors, and optimal treatment

The presence of stones within the kidneys, or nephrolithiasis, is the most common condition affecting the urinary system.1 Kidney stones correlate with an increased risk of chronic kidney disease, end-stage renal failure, cardiovascular diseases, diabetes, and hypertension.2

To optimize patient care, our kidney stone services identify the physical compositions of stones. Mayo Clinic Laboratories’ comprehensive approach and concise reporting support your practice with accurate interpretation to guide clinical decisions.

Kidney stones TEST MENU

Kidney stones

Key testing


  • Identifies renal calculi composition.
  • AI-assisted results interpretation.
  • Supports treatment plan development.
  • Aids in reducing stone recurrences.

More information

Cutting-edge interpretation

To characterize the kidney stone FTIR spectra, Mayo Clinic developed and validated a suite of novel artificial intelligence (AI) algorithms to help interpret the FTIR spectra. For common or easy spectra that meet defined quality and accuracy criteria, these results can be automatically released, while more challenging spectra are passed along for a technologist to review before results are finalized. 

This new AI-augmented analysis results in improved accuracy and efficiency of the clinical workflow to ensure physicians are provided the correct results, enabling proper guidance on treatment options to prevent future stone recurrence.3

When to consider testing

  • After first collected stone.
  • After subsequent stones, depending on the clinical situation.
  • In conjunction with a metabolic evaluation.

Specimen requirements

  • Specimen type: Stone
  • Supplies: Stone Analysis Collection Kit (T550)
  • Sources: Bladder, kidney, prostatic, renal, or urinary
  • Specimen volume: Entire dried calculi specimen


  1. Nojaba L, Guzman N. Nephrolithiasis. In: StatPearls. Treasure Island (FL): StatPearls Publishing; August 8, 2022.
  2. Alelign T, Petros B. Kidney stone disease: an update on current concepts. Adv Urol. 2018;2018:3068365.
  3. Day P, Erdahl S, Rokke D, et al. Artificial intelligence for kidney stone spectra analysis: using artificial intelligence algorithms for quality assurance in the clinical laboratory. Mayo Clin Proc Digital Health. March 2023;1(1):1-12.

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