Deep learning architectures in the prediction of acute appendicitis and perforated appendicitis: A narrative review

Basit Öğe Kaydı
dc.contributor.authorÇolak, Cemil
dc.contributor.authorAkbulut, Sami
dc.date.accessioned2026-04-04T13:14:32Z
dc.date.available2026-04-04T13:14:32Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractAcute appendicitis remains a significant cause of acute abdominal pain requiring emergency intervention. Timely and accurate differentiation between uncomplicated and complicated (perforated) acute appendicitis is critical for optimizing patient management and minimizing morbidity and mortality rates. Conventional diagnostic techniques, such as clinical assessment, biochemical analysis, and imaging procedures, sometimes encounter difficulties in differentiating uncomplicated from complicated appendicitis due to overlapping clinical characteristics and interobserver variability. Recent advancements in deep learning (DL) architectures have transformed medical diagnostics, offering new opportunities for more precise disease classification. Convolutional neural networks (CNNs) and other DL-based models have demonstrated significant potential in analyzing radiological images, improving diagnostic accuracy, and reducing false negatives. These models can extract subtle imaging features that may not be easily identifiable by human evaluation, thus enhancing early detection and guiding timely surgical intervention. This narrative review explores the role of DL in differentiating uncomplicated and complicated appendicitis, assessing current methodologies, their performance metrics, limitations, and clinical implications. The findings highlight the potential for DL to revolutionize appendicitis diagnostics, ultimately contributing to improved patient outcomes and streamlined clinical workflows.
dc.identifier.doi10.5455/medscience.2025.03.085
dc.identifier.endpage953
dc.identifier.issn2147-0634
dc.identifier.issue3
dc.identifier.startpage948
dc.identifier.trdizinid1344945
dc.identifier.urihttps://doi.org/10.5455/medscience.2025.03.085
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1344945
dc.identifier.urihttps://hdl.handle.net/11616/107300
dc.identifier.volume14
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMedicine Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250329
dc.subjectAcil Tıp
dc.subjectCerrahi
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titleDeep learning architectures in the prediction of acute appendicitis and perforated appendicitis: A narrative review
dc.typeArticle

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu