دورية أكاديمية

Neural Network Analysis of Crystalluria Content to Predict Urinary Stone Type

التفاصيل البيبلوغرافية
العنوان: Neural Network Analysis of Crystalluria Content to Predict Urinary Stone Type
المؤلفون: Almannie RM, Alsufyani AK, Alturki AU, Almuhaideb M, Binsaleh S, Althunayan AM, Alomar MA, Albarraq KM, Alyami FA
المصدر: Research and Reports in Urology, Vol Volume 13, Pp 867-876 (2021)
بيانات النشر: Dove Medical Press, 2021.
سنة النشر: 2021
المجموعة: LCC:Diseases of the genitourinary system. Urology
مصطلحات موضوعية: crystalluria, urinary sediment, neural network, urolithiasis, stone disease, urinary crystals, Diseases of the genitourinary system. Urology, RC870-923
الوصف: Raed M Almannie, Abdullah K Alsufyani, Abdullah U Alturki, Mana Almuhaideb, Saleh Binsaleh, Abdulaziz M Althunayan, Mohammed A Alomar, Khalid M Albarraq, Fahad A Alyami Division of Urology, Department of Surgery, College of Medicine and King Saud University Medical City, King Saud University, Riyadh, Saudi ArabiaCorrespondence: Abdullah K AlsufyaniDivision of Urology, Department of Surgery, King Saud University Medical City, King Saud University, Saudi Arabia, King Saud University, Riyadh, 11451, Saudi ArabiaTel +966114692731Fax +966114679493Email abdsuf@gmail.comPurpose: To investigate the relationship between urinary stone type and the type of crystals in the urine.Patients and Methods: This 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.Results: Of 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.Conclusion: The 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.Keywords: crystalluria, urinary sediment, neural network, urolithiasis, stone disease, urinary crystals
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2253-2447
Relation: https://www.dovepress.com/neural-network-analysis-of-crystalluria-content-to-predict-urinary-sto-peer-reviewed-fulltext-article-RRU; https://doaj.org/toc/2253-2447
URL الوصول: https://doaj.org/article/b7c68b870c854fa3bfce6330c2ed1809
رقم الأكسشن: edsdoj.b7c68b870c854fa3bfce6330c2ed1809
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