From body composition to reflux esophagitis: an interpretable machine learning model based on CT-derived features

Background The prevalence of gastroesophageal reflux disease (GERD) has been increasing in China. Previous studies link sarcopenia and visceral adiposity to GERD, but most models lack CT-based body composition data. This study aims to improve the identification of Reflux esophagitis (RE) by applying machine learning (ML) to third lumbar vertebra cross-sectional CT (L3-CT) images for quantitative analysis of muscle and fat mass. Methods Participants underwent comprehensive abdominal CT and gastro