ArticleHealth science reports2025
Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Research on the construction of an artificial intelligence model for the early diagnosis of pediatric acute appendicitis: a single-center retrospective study based on complete blood count.BMC pediatrics · 2026Article
- Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- A hybrid machine learning approach to improve the diagnostic accuracy of acute appendicitis.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications.World journal of gastroenterology · 2025Review
- Machine Learning and Feature Selection in Pediatric Appendicitis.Tomography (Ann Arbor, Mich.) · 2025Article
- Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study.Children (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Appendicitis is the most common surgical emergency in pediatric patients, requiring timely diagnosis to prevent complications. This study introduces an innovative approach by integrating clinical, laboratory, and imaging features with advanced machine-learning techniques to enhance diagnostic accuracy in pediatric appendicitis. Methods: A retrospective analysis was conducted on 782 pediatric patients from the Regensburg Pediatric Appendicitis Data set. Clinical scores, laboratory markers, and imaging findings were analyzed. Statistical comparisons were performed using independent Results: Significant differences were observed in clinical scores (e.g., Alvarado Score and Pediatric Appendicitis Score) and laboratory markers (e.g., WBC count and neutrophil percentage) between appendicitis (AA) and non-appendicitis (Non-AA) groups ( Conclusion: This study represents a novel application of machine learning models, particularly Random Forest, to enhance diagnostic accuracy for pediatric appendicitis. The integration of clinical, laboratory, and imaging features offers a comprehensive and precise diagnostic framework. Further validation in diverse populations is recommended.
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