Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications

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dc.contributor.authorAkbulut, Sami
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorColak, Cemil
dc.date.accessioned2026-04-04T13:30:53Z
dc.date.available2026-04-04T13:30:53Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractAcute appendicitis (AAp) remains one of the most common abdominal emergencies, requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries. Conventional diagnostic methods, including medical history, clinical assessment, biochemical markers, and imaging techniques, often present limitations in sensitivity and specificity, especially in atypical cases. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning (ML) and deep learning (DL) models. This review evaluates the current applications of AI in both adult and pediatric AAp, focusing on clinical data-based models, radiological imaging analysis, and AI-assisted clinical decision support systems. ML models such as random forest, support vector machines, logistic regression, and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems, achieving sensitivity and specificity rates exceeding 90% in multiple studies. Additionally, DL techniques, particularly convolutional neural networks, have been shown to outperform radiologists in interpreting ultrasound and computed tomography images, enhancing diagnostic confidence. This review synthesized findings from 65 studies, demonstrating that AI models integrating multimodal data including clinical, laboratory, and imaging parameters further improved diagnostic precision. Moreover, explainable AI approaches, such as SHapley Additive exPlanations and local interpretable model-agnostic explanations, have facilitated model transparency, fostering clinician trust in AI-driven decision-making. This review highlights the advancements in AI for AAp diagnosis, emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases. While preliminary results are promising, further prospective, multicenter studies are required for large-scale clinical implementation, given that a great proportion of current evidence derives from retrospective designs, and existing prospective cohorts exhibit limited sample sizes or protocol variability. Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
dc.identifier.doi10.3748/wjg.v31.i43.112000
dc.identifier.issn1007-9327
dc.identifier.issn2219-2840
dc.identifier.issue43
dc.identifier.pmid41358178
dc.identifier.scopus2-s2.0-105022124004
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3748/wjg.v31.i43.112000
dc.identifier.urihttps://hdl.handle.net/11616/108448
dc.identifier.volume31
dc.identifier.wosWOS:001630941300011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBaishideng Publishing Group Inc
dc.relation.ispartofWorld Journal of Gastroenterology
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectAcute appendicitis
dc.subjectComplicated appendicitis
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectDecision support systems
dc.subjectExplainable artificial intelligence
dc.subjectPredictive modeling
dc.subjectDiagnosis
dc.titleArtificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications
dc.typeReview

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