Innovative diagnostic tool: Convolutional neural network for early fat malabsorption detection in pediatric patients with chronic diarrhea

Abstract

Background: Chronic diarrhea in children poses a significant clinical challenge and can lead to adverse health outcomes. Among various causes, fat malabsorption is particularly concerning, as it may lead to inadequate nutrient absorption, malnutrition, and impaired growth. Prompt and precise diagnosis is crucial for implementing effective treatments. Objectives: The goal of this study is to utilize deep learning to create a superior diagnostic tool that exceeds traditional methods, facilitating the early identification of fat malabsorption in children suffering from chronic diarrhea. Methods: In a preliminary study involving 100 pediatric patients, 25 machine learning algorithms were evaluated. The convolutional neural network (CNN) was identified as the most effective and subsequently refined through hyperparameter tuning. Results: The CNN model exhibited exceptional performance, attaining a test accuracy of 97% and an area under the curve (AUC) score of 99.4%. These results underscore its reliability in accurately identifying cases of fat malabsorption. Conclusions: This research represents noteworthy progress in pediatric gastroenterology, merging deep learning techniques with medical expertise to develop a dependable and rapid diagnostic tool. This innovative method promises significant improvements in detecting fat malabsorption, potentially transforming clinical practices and enhancing patient outcomes in children with chronic diarrhea.