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August 13, 2025The Open Urology & Nephrology JournalOpen Access

From Traditional Statistics to Artificial Intelligence: Advancing Pediatric UTI Recurrence Prediction in Low-Resource Communities

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Authors

MAMohammed J AboudMKManal KadhimSKShaimaa Kadhim

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Overview

Retrospective cohort study evaluated AI's impact on UTI recurrence prediction in children, highlighting healthcare disparities.

Key Points

  • The deep learning model achieved an AUC-ROC of 0.94, indicating high predictive accuracy for pediatric UTI recurrence.
  • SHAP analysis revealed critical predictors for UTI recurrence including vesicoureteral reflux grade ≥3 and rural residence.
  • A retrospective cohort study analyzed 211 pediatric UTI cases from a single center in Iraq to develop predictive models.
  • AI-based models outperform traditional statistics, emphasizing the importance of integrating AI into healthcare systems.

Cite This Study

Aboud et al. (2025) studied this question.

synapsesocial.com/papers/68af4551ad7bf08b1ead35edhttps://doi.org/10.2174/011874303x408497250811053444
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Also Consider

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  1. 1Predicting vesicoureteral reflux outcomes using artificial intelligence: A critical appraisal using APPRAISE-AI2026 · 3 citations
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  4. 4Integrating Risk Factors and Symptoms for Urinary Tract Infection Diagnosis Using an Explainable AI Approach in Low-Resource Regions2026
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