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

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dc.contributor.authorKeskinoglu, Ahmet
dc.contributor.authorOzgur, Su
dc.date.accessioned2020-12-01T11:58:26Z
dc.date.available2020-12-01T11:58:26Z
dc.date.issued2020
dc.departmentEge Üniversitesien_US
dc.description.abstractAim: 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.en_US
dc.description.sponsorshipTUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114S011]en_US
dc.description.sponsorshipThis article was written based on a project supported by TUBITAK with project number 114S011.en_US
dc.identifier.doi10.4274/jpr.galenos.2019.24650
dc.identifier.endpage235en_US
dc.identifier.issn2147-9445
dc.identifier.issn2147-9445en_US
dc.identifier.issue3en_US
dc.identifier.startpage230en_US
dc.identifier.urihttps://doi.org/10.4274/jpr.galenos.2019.24650
dc.identifier.urihttps://hdl.handle.net/11454/62021
dc.identifier.volume7en_US
dc.identifier.wosWOS:000544838900009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherGalenos Yayinciliken_US
dc.relation.ispartofJournal of Pediatric Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectdifferential diagnosisen_US
dc.subjecturinary tract infectionen_US
dc.subjecturinary ultrasonographyen_US
dc.subjectvesicoureteral refluxen_US
dc.titleThe Use of Artificial Neural Networks for Differential Diagnosis between Vesicoureteral Reflux and Urinary Tract Infection in Childrenen_US
dc.typeArticleen_US

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