Inflammatory indices, machine learning and artificial intelligence in tubal ectopic pregnancy management makine öğrenmesi ve yapay zeka

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dc.contributor.authorZorlu, Ugurcan
dc.contributor.authorDuz, Senem Arda
dc.contributor.authorKurtaran, Gul
dc.contributor.authorHalilzade, Mohammad Ibrahim
dc.contributor.authorElmas, Burak
dc.date.accessioned2026-04-04T13:30:49Z
dc.date.available2026-04-04T13:30:49Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractObjective: To assess the predictive value of hematologic and biochemical inflammatory indices for methotrexate (MTX) treatment outcomes in tubal ectopic pregnancy (TEP) and to develop machine learning (ML) models for individualized risk stratification. Materials and Methods: This retrospective cohort included 293 hemodynamically stable TEP patients who were treated with a single dose of MTX between January 2019 and December 2023. Demographic, clinical, ultrasonographic, and laboratory data were analyzed. Inflammatory indices- including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), and fibrinogen-to-albumin ratio (FAR)-were calculated. Outcomes were categorized as single-dose MTX success, requirement for additional MTX, or surgery. Predictive accuracy of five supervised ML algorithms was evaluated using receiver operating characteristic analysis. Results: Single-dose MTX was successful in 65.5% of patients; 18.4% required an additional dose, and 16.0% underwent surgery. AISI had the highest predictive accuracy for surgery [area under the curve (AUC)=0.929], followed by SIRI (AUC=0.899) and FAR (AUC=0.847). NLR best predicted the need for additional MTX (AUC=0.675). Na & iuml;ve Bayes achieved the highest performance for surgical prediction (accuracy=98.3%, AUC=0.998), while random forest and gradient boosting were most effective in predicting the need for additional MTX (accuracy=83.1%, AUC=0.884-0.896). Feature importance analyses consistently ranked AISI, SIRI, and FAR as top predictors. Conclusion: AISI, SIRI, and FAR are strong predictors of MTX failure and surgical intervention in TEP. Combining these biomarkers with ML models markedly improves predictive performance and supports a personalized approach to TEP management. Multicenter prospective validation is needed before clinical application.
dc.identifier.doi10.4274/tjod.galenos.2026.37165
dc.identifier.endpage55
dc.identifier.issn2149-9322
dc.identifier.issn2149-9330
dc.identifier.issue1
dc.identifier.orcid0000-0002-8912-0812
dc.identifier.pmid41717887
dc.identifier.scopus2-s2.0-105032701135
dc.identifier.scopusqualityQ3
dc.identifier.startpage47
dc.identifier.urihttps://doi.org/10.4274/tjod.galenos.2026.37165
dc.identifier.urihttps://hdl.handle.net/11616/108385
dc.identifier.volume23
dc.identifier.wosWOS:001710395500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherGalenos Publ House
dc.relation.ispartofTurkish Journal of Obstetrics and Gynecology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectEctopic pregnancy
dc.subjectinflammation
dc.subjectinflammatory markers
dc.subjectmachine learning
dc.subjectmethotrexate
dc.titleInflammatory indices, machine learning and artificial intelligence in tubal ectopic pregnancy management makine öğrenmesi ve yapay zeka
dc.typeArticle

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