Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study

Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study

<b>Background</b>: Accurate diagnosis of acute appendicitis in children remains challenging due to variable presentations and limitations of existing clinical scoring systems. While machine learning (ML) offers a promising approach to enhance diagnostic precision, most prior studies have...

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Main Authors: Emrah Aydın, Taha Eren Sarnıç, İnan Utku Türkmen, Narmina Khanmammadova, Ufuk Ateş, Mustafa Onur Öztan, Tamer Sekmenli, Necip Fazıl Aras, Tülin Öztaş, Ali Yalçınkaya, Murat Özbek, Deniz Gökçe, Hatice Sonay Yalçın Cömert, Osman Uzunlu, Aliye Kandırıcı, Nazile Ertürk, Alev Süzen, Fatih Akova, Mehmet Paşaoğlu, Egemen Eroğlu, Gülnur Göllü Bahadır, Ahmet Murat Çakmak, Salim Bilici, Ramazan Karabulut, Mustafa İmamoğlu, Haluk Sarıhan, Süleyman Cüneyt Karakuş
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Children
Subjects:
appendicitis
pediatrics
machine learning
diagnosis
random forest
clinical decision support
Online Access:https://www.mdpi.com/2227-9067/12/7/937
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