Utilizing Machine Learning for Enhanced Diagnosis and Management of Pediatric Appendicitis: A Multilayer Neural Network Approach

Abstract

This study focuses on pediatric appendicitis, a leading cause of hospital admissions due to abdominal pain in children, characterized by a substantial risk of perforation, especially in younger patients. Traditional diagnostic methods, while effective, often lack specificity and are sup plemented by varying laboratory and imaging techniques. This research introduces a novel application of machine learning (ML), specifically a multi multi-output neural network model, to address the complexities of diagnosing appendicitis, determining its severit y, and guiding management strategies in pediatric cases. The model, with its unique architecture, has been trained and tested on a comprehensive dataset from Children’s Hospital St. Hedwig in Regensburg, Germany, which includes a wide array of clinical dat a and ultrasound images. The results demonstrate remarkable accuracy in classifying management approaches, severity levels, and diagnosis, highlighting the model's potential in supporting clinical decisiondecision-making. While not a replacement for clinical judgm ent, this model serves as a promising tool in the ongoing efforts to improve pediatric appendicitis care, offering a glimpse into the future of AI AI-enhanced medical diagnostics and treatment planning.