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Related Concept Videos

Gastroesophageal Reflux Disease01:25

Gastroesophageal Reflux Disease

Gastroesophageal reflux disease (GERD) is the backward flow of stomach contents (acid, pepsin, or bile) into the esophagus, causing mucosal inflammation known as esophagitis. It results from failure of antireflux mechanisms, mainly the lower esophageal sphincter (LES), influenced by mechanical and physiological factors.Etiology and Risk FactorsGERD develops when LES function is weakened or when intra-abdominal pressure increases. Risk factors include aging, obesity, and sliding hiatal hernia,...
Gastroesophageal Reflux Disease II: Clinical Features and Management01:29

Gastroesophageal Reflux Disease II: Clinical Features and Management

Gastroesophageal reflux disease, or GERD, is a persistent medical condition that affects many individuals worldwide. Its clinical manifestations can vary greatly, making diagnosis and management challenging for healthcare professionals. The following is a comprehensive overview of the clinical manifestations, assessment, and management strategies for GERD.
Clinical Manifestations
GERD presents itself in a multitude of ways, with symptoms varying from person to person. The hallmark symptoms are...

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Related Experiment Video

Updated: May 26, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Published on: September 19, 2025

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

Tianyi Wang1, Lu Chen2, Yajie Li1,3

  • 1Medical Faculty, Southeast University, Nanjing, Jiangsu, China.

Frontiers in Physiology
|May 25, 2026
PubMed
Summary

Low skeletal muscle mass index (SMI) is linked to reflux esophagitis (RE). Machine learning models using CT scans identified key body composition factors for RE risk assessment.

Keywords:
IMATbody compositionlow SMImachine learningrandom forestreflux esophagitis

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Published on: October 10, 2018

Area of Science:

  • Radiology and Medical Imaging
  • Gastroenterology
  • Computational Biology

Background:

  • Gastroesophageal reflux disease (GERD) prevalence is rising in China.
  • Previous research links sarcopenia and visceral adiposity to GERD.
  • Existing models often lack CT-based body composition data.

Purpose of the Study:

  • To enhance Reflux esophagitis (RE) identification using machine learning (ML).
  • To quantitatively analyze muscle and fat mass from L3-CT images.
  • To explore the association between body composition and RE.

Main Methods:

  • Abdominal CT and gastroscopy were performed on participants.
  • Body composition parameters (SM, FM, FFM, VAT, SAT, IMAT) were derived from L3-CT images.
  • Six ML models (RF, XGBoost, LR, KNN, SVM, ANN) were developed and evaluated using AUROC; SHAP values interpreted the RF model.

Main Results:

  • 135 out of 324 subjects had RE; low SMI was more prevalent in the RE group (52.6% vs. 36.5%).
  • Top predictors for RE in the RF model included IMAT, VSR, age, VAT, SAT, and hiatal hernia.
  • RF and LR models achieved the best discriminative performance (AUC=0.829) in the validation set.

Conclusions:

  • A significant association exists between low skeletal muscle mass index (SMI) and reflux esophagitis (RE).
  • Machine learning models effectively identified crucial body composition factors related to RE.
  • These findings offer insights for targeted screening and clinical assessment of RE.