A Laboratory Decision-Support System for Reflective Urine Culture Testing: Development of an Interpretable Artificial Intelligence Model Reflektif İdrar Kültürü Testleri için Bir Laboratuvar Karar Destek Sistemi: Yorumlanabilir Bir Yapay Zekâ Modelinin Geliştirilmesi

Demirci F., Akşit M., Akbulut İ., Demirci A.

Mediterranean Journal of Infection, Microbes and Antimicrobials, cilt.15, sa.1, 2026 (ESCI, Scopus, TRDizin)

Özet

Introduction: Urinary tract infections are a common diagnostic challenge. Although urine culture remains the gold standard, it is time-consuming and often ordered reflexively. This study aimed to develop and validate an interpretable machine-learning–based Laboratory Decision-Support System (LDSS) to guide reflective urine culture prioritization using only structured laboratory data. Materials and Methods: We analyzed a retrospective cohort of 51,923 adult patients. Seven machine learning algorithms were trained, with the Random Forest (RF) model demonstrating the highest accuracy. SHapley Additive exPlanations was employed to ensure model interpretability. A reduced RF model, using the top 10 predictive features, was used to construct three scoring systems: one emphasizing model fidelity, one optimizing diagnostic balance, and one maximizing sensitivity. Results: The RF model demonstrated excellent performance (external receiver operating characteristic – area under the curve [ROC-AUC]: 0.956). The simplified 10-variable model maintained high accuracy (ROC-AUC: 0.947). Key predictors included bacterial count, leukocyte count, nitrite presence, and patient age. The scoring systems offered flexible options tailored to different diagnostic priorities, with the SAFE-Score achieving 95.3% sensitivity. Conclusion: The developed LDSS supports rational antibiotic use by reducing unnecessary culture testing. Its explainable structure facilitates collaboration between laboratory professionals and clinicians, contributing to standardized reflective testing workflows and interdisciplinary decision-making and strengthens antimicrobial stewardship, while preserving the central role of urine culture in infection management.