ArticleResearch and reports in urology2021
Neural Network Analysis of Crystalluria Content to Predict Urinary Stone Type.
Article in Research and reports in urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Bariatric surgery does not affect kidney stone disease.Scientific reports · 2024Article
- Comprehensive prediction of urolithiasis based on clinical factors, blood chemistry and urinalysis: UROLITHIASIS score.Scientific reports · 2023Article
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9 authors.
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Abstract
purposeTo investigate the relationship between urinary stone type and the type of crystals in the urine. PATIENTS AND
methodsThis retrospective study involved 485 patients with urinary stones treated at King Saud University Medical City from May 2015 to June 2017. Clinical data were obtained from medical records. Different statistical analysis methods were applied, including basic contingency analysis, analysis of variance, logistic regression, discriminant analysis, partition modeling, and neural network evaluations.
resultsOf 485 patients, 47 had crystals detected by urinalysis. The most common type of crystal was calcium oxalate (n = 31), which had the highest association with calcium oxalate stones. Uric acid crystals (n = 8) were associated with uric acid stones. The neural network model used for determining the sensitivity and specificity showed an R-square value of 0.88, with an area under the curve of 0.94 for calcium oxalate, 0.94 for carbonate apatite, and 1.0 for uric acid.
conclusionThe predictive algorithm developed in the present study may be used with a patient's clinical parameters to predict the stone type. This approach predicts the stone types associated with certain patient characteristics with a high sensitivity and specificity, indicating that the models may be a valuable clinical tool in the diagnosis, management, and monitoring of stone diseases.
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