Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network

Abdović, Slaven; Ćuk, Martin; Cekada, N; Milošević, Milan; Geljić, Antonella; Fusić, Snježana; Bastić, Mislav; Bahtijarević, Zoran

doi:10.1007/s00345-018-2588-9

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…Articles published in recent years show that machine learning models have excellent performance when used in cardiovascular disease detection, medical imaging identification, and other areas (7)(8)(9), and some studies have applied these models in urology. It has been reported that an artificial neural network can enhance the accuracy of predicting posterior urethral obstruction with lower urinary tract symptoms, but only 201 patients from one center were included (25). However, an artificial neural network usually requires substantial amounts of data, at least thousands of samples, to build a stable model.…”

Section: Discussionmentioning

confidence: 99%

Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi

He¹,

Peng²,

Ying³

et al. 2022

Transl Androl Urol

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Background: Quick and accurate identification of urinary calculi patients with positive urinary cultures is critical to the choice of the treatment strategy. Predictive models based on machine learning algorithms provide a new way to solve this problem. This study aims to determine the predictive value of machine learning algorithms using a urine culture predictive model based on patients with urinary calculi.Methods: Data were collected from four clinical centers in the period of June 2016, to May 2019. 2,054 cases were included in the study. The dataset was randomly split into ratios of 5:5, 6:4, and 7:3 for model construction and validation. Predictive models of urine culture outcomes were constructed and validated by logistic regression, random forest, adaboost, and gradient boosting decision tree (GBDT) models. Each ratio's construction and verification were repeated five times independently for cross-validation. The Matthews correlation coefficient (MMC), F1-score, receiver operating characteristic (ROC) curve with the area under curve (AUC) was used to evaluate the performance of each prediction model. The additive net reclassification index (NRI) and absolute NRI were used to assess the predictive capabilities of the models.Results: Four prediction models of urinary culture results in patients with urinary calculi were constructed.The mean AUCs of the logistic regression, random forest, adaboost, and GBDT models were 0.761 (95%

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Section: Discussionmentioning

confidence: 99%

Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi

He¹,

Peng²,

Ying³

et al. 2022

Transl Androl Urol

View full text Add to dashboard Cite

show abstract

“…This tool has already been modified for children from a data set of 1060 children [ 72 ]. In CAKUT diseases, AI models are still rare [ 73 , 74 , 75 ]. Very recently, the first ML model to predict outcomes in PUV has been proposed and made available as an online tool [ 76 ].…”

Section: Prognostic Factors For Chronic Kidney Disease Developmentmentioning

confidence: 99%

Chronic Kidney Disease in Boys with Posterior Urethral Valves–Pathogenesis, Prognosis and Management

Klaus

Lange-Sperandio

2022

Biomedicines

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Posterior urethral valves (PUV) are the most common form of lower urinary tract obstructions (LUTO). The valves can be surgically corrected postnatally; however, the impairment of kidney and bladder development is irreversible and has lifelong implications. Chronic kidney disease (CKD) and bladder dysfunction are frequent problems. Approximately 20% of PUV patients will reach end-stage kidney disease (ESKD). The subvesical obstruction in PUV leads to muscular hypertrophy and fibrotic remodelling in the bladder, which both impair its function. Kidney development is disturbed and results in dysplasia, hypoplasia, inflammation and renal fibrosis, which are hallmarks of CKD. The prognoses of PUV patients are based on prenatal and postnatal parameters. Prenatal parameters include signs of renal hypodysplasia in the analysis of fetal urine. Postnatally, the most robust predictor of PUV is the nadir serum creatinine after valve ablation. A value that is below 0.4 mg/dl implies a very low risk for ESKD, whereas a value above 0.85 mg/dl indicates beneficial kidney outcome. Experimental urinary markers such as MCP-1 and TGF-β, as well as microalbuminuria, indicate progression to CKD. Until now, prenatal intervention may improve survival but yields no renal benefit. The management of PUV patients includes control of bladder dysfunction and CKD treatment to slow down progression by controlling hypertension, proteinuria and infections. In kidney transplantation, aggressive bladder management is essential to ensure optimal graft survival.

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Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves

et al. 2021

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Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network

Cited by 15 publications

References 22 publications

Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi

Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi

Chronic Kidney Disease in Boys with Posterior Urethral Valves–Pathogenesis, Prognosis and Management

Posterior Urethral Valves Outcomes Prediction (PUVOP): a machine learning tool to predict clinically relevant outcomes in boys with posterior urethral valves

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