The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

Serrano-Durbá, A.; Serrano, Adela; Magdalena‐Benedito, Rafael; Martín, Juan Enrique Pérez; Soria, Emilio; Domínguez, Carlos; Estornell, F.; García-Ibarra, F

doi:10.1111/j.1464-410x.2004.04912.x

Cited by 22 publications

(15 citation statements)

References 23 publications

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“…Our endpoints should go beyond short-term evidence of resolution and focus on patient morbidity, treatment risks and benefits, and impact on long-term consequences such as hypertension and renal insufficiency. Finally, similar to the direction adopted for other genitourinary disorders, the development of validated nomograms or neural networks [76,77] may help select patients at risk for particular adverse outcomes or those who would benefit from one treatment option over another.…”

Section: Resultsmentioning

confidence: 98%

Endoscopic treatment of reflux: management pros and cons

Lorenzo

Khoury

2006

Current Opinion in Urology

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The management of vesicoureteral reflux has changed dramatically in the past decade, mostly because of the increasing acceptance of endoscopic injection therapy as an adequate, minimally invasive, and effective form of therapy. Recent advances in the composition of injectable materials have allowed for easier placement with a perceived favorable safety profile. In particular, dextranomer/hyaluronic acid has become the injectable material of choice, with quick acceptance and widespread use soon after its introduction in different countries. As we critically evaluate the evolving treatment options, the presented literature helps draw attention to some of the challenges we face and the need for long-term and carefully planned prospective studies to support our interventions.

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Section: Resultsmentioning

confidence: 98%

Endoscopic treatment of reflux: management pros and cons

Lorenzo

Khoury

2006

Current Opinion in Urology

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“…The high-risk group identified by this model can benefit from systemic treatment post cystectomy to improve their disease related morbidity and mortality [ 95 , 96 ]. The 5 years survival post cystectomy was the output of 2 other ANN with a high prediction efficacy of 77% and 90% respectively (Table 6 ) [ 97 , 99 ].…”

Section: Resultsmentioning

confidence: 99%

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

Salem

Soria

Lund

et al. 2021

BMC Med Inform Decis Mak

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Background Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. Methods The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. Results The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. Conclusion ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

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“…Some studies reported that ANN and other data mining methods supported medical decisions regarding VUR and some nephrological problems (21)(22)(23)(24)(25)(26)(27).…”

Section: Discussionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Differential Diagnosis between Vesicoureteral Reflux and Urinary Tract Infection in Children

Keskınoğlu¹,

Özgür²

2020

jpr

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Vesicoureteral reflux (VUR) and urinary tract infection (UTI) are common problems in children. Our goal is to use different models for the clinical decision of differential diagnosis of VUR and UTI in children. Materials and Methods: This was a retrospective cross-sectional study with 611 pediatric patients enrolled. Detailed information for the patients was obtained from hospital records and patient files. Three models including different variables were evaluated via an artificial neural network for the differential diagnosis of VUR and recurrent UTI. Clinical findings were included in Model 1, clinical and laboratory findings were included in Model 2, and clinical, laboratory and detailed urinary ultrasonography (USG) findings were included in Model 3. A cross-validation technique was used to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. Results: Of the 611 children, 425 (69.6%) had VUR and 186 (30.4%) had UTI. The sensitivity of Model 1 and Model 2 were 0.682 and 0.856, respectively. Also, Model 3 showed the best performance and highest sensitivity with 0.939 for differential diagnosis. Conclusion: Differential diagnosis between VUR and UTI in children can be predicted by using clinical, laboratory and USG variables via an Artificial Neural Network. Model 3, which included clinical, laboratory and USG variables together, showed the best performance and highest sensitivity.

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The use of neural networks for predicting the result of endoscopic treatment for vesico‐ureteric reflux

Cited by 22 publications

References 23 publications

Endoscopic treatment of reflux: management pros and cons

Endoscopic treatment of reflux: management pros and cons

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

The Use of Artificial Neural Networks for Differential Diagnosis between Vesicoureteral Reflux and Urinary Tract Infection in Children

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