ArticleMayo Clinic proceedings. Digital health2023
Artificial Intelligence for Kidney Stone Spectra Analysis: Using Artificial Intelligence Algorithms for Quality Assurance in the Clinical Laboratory.
Article in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Explainable AI in kidney stone detection and segmentation: a mini review.Frontiers in digital health · 2026Review
- Still counting sperm? Why novel, truly informative measurements of testis function in male infertility are urgently needed.Endocrine · 2025Review
- Use of Artificial Intelligence for Analyzing Kidney Stone Composition: Are We There Yet?Mayo Clinic proceedings. Digital health · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To determine if a set of artificial intelligence (AI) algorithms could be leveraged to interpret Fourier transform infrared spectroscopy (FTIR) spectra and detect potentially erroneous stone composition results reported in the laboratory information system by the clinical laboratory. Background: Nephrolithiasis (kidney stones) is highly prevalent, causes significant pain, and costs billions of dollars annually to treat and prevent. Currently, FTIR is considered the reference method for clinical kidney stone constituent analysis. This process, however, involves human interpretation of spectra by a qualified technologist and is susceptible to human error. Methods: This prospective validation study was conducted from October 29, 2020, to October 28, 2021, to test if the addition of AI algorithm overreads to FTIR spectra could improve the detection rate of technologist-misclassified FTIR spectra. The preceding year was used as a control period. Disagreement between the AI overread and technician interpretation was resolved by an independent human interpretation. The rate of verified human misclassifications that resulted in revised reported results was the primary end point. Results: Spectra of 81,517 kidney stones were reviewed over the course of 1 year. The overall clinical concordance between the technologist and algorithm was 90.0% (73,388/81,517). The report revision rate during the AI implementation period was nearly 8 times higher than that during the control period (relative risk, 7.9; 95% CI, 4.1-15.2). Conclusion: This study demonstrated that an AI quality assurance check of human spectra interpretation resulted in the identification of a significant increase in erroneously classified spectra by clinical laboratory technologists.
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Registered trials
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