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Related Concept Videos

Gastroesophageal Reflux Disease II: Clinical Features and Management01:29

Gastroesophageal Reflux Disease II: Clinical Features and Management

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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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Gastroesophageal Reflux Disease I: Meaning and Pathophysiology01:29

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Gastroesophageal Reflux Disease (GERD) involves the recurrent backflow of the stomach or duodenal contents into the esophagus, leading to troublesome symptoms and potential esophageal mucosal damage. Although GERD is often referred to as a disease, it is more accurately described as a syndrome, as it encompasses a range of symptoms and complications rather than a singular pathological entity, impacting a large number of individuals as the most prevalent upper gastrointestinal problem. Roughly...
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Endoscopic Procedures I: Esophagogastroduodenoscopy01:29

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An Esophagogastroduodenoscopy (EGD) is a diagnostic procedure in which an endoscopist uses a flexible, lighted endoscope to visualize the upper gastrointestinal (GI) tract. The procedure includes visualizing the oropharynx, esophagus, stomach, and the first part of the small intestine, the duodenum.
During an EGD, the endoscope can be used to:
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Barrett Esophagus-I: Introduction01:21

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Barrett's esophagus is a medical condition where the esophageal mucosa is significantly damaged by stomach acid or other digestive fluids, often due to long-term exposure associated with gastroesophageal reflux disease (GERD). In GERD, a weakened or abnormally relaxed lower esophageal sphincter allows stomach acid to flow persistently into the esophagus.
This constant acid exposure transforms the esophagus's pink mucosal lining (stratified squamous epithelium) into a type of lining more...
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Barrett Esophagus-II: Clinical Manifestations and Management01:21

Barrett Esophagus-II: Clinical Manifestations and Management

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Individuals with Barrett's esophagus are often asymptomatic, but they may experience symptoms commonly associated with GERD, such as heartburn and acid regurgitation. Additional symptoms can include difficulty swallowing, chest pain, unintentional weight loss, blood in the stool (which may appear black, tarry, or bloody), and episodes of vomiting.
To diagnose Barrett's esophagus, healthcare providers often recommend an endoscopy for those showing symptoms of acid reflux. The procedure...
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Related Experiment Video

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An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning

Hsu-Heng Yen1,2,3, Hui-Yu Tsai4, Chi-Chih Wang5,6

  • 1Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan.

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|November 26, 2022
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Summary

This study introduces GerdNet-RF, an AI model that improves the accuracy of diagnosing Gastroesophageal reflux disease (GERD) severity from endoscopic images. The AI model enhances diagnostic capabilities, leading to better patient care.

Keywords:
deep learninggastroesophageal reflux diseasehealthcaremachine learningtransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Gastroenterology

Background:

  • Gastroesophageal reflux disease (GERD) diagnosis relies on the Los Angeles classification for treatment.
  • Artificial intelligence (AI) and deep learning show promise in assisting clinical diagnoses.

Purpose of the Study:

  • To develop an AI-driven, two-stage classification model for endoscopic GERD diagnosis.
  • To enhance the accuracy of GERD severity classification using deep learning and machine learning.

Main Methods:

  • A two-stage approach combining transfer learning for feature extraction and machine learning for classification.
  • Application of transfer learning to extract precise image features from GERD endoscopic images.
  • Development of a classification model (GerdNet-RF) integrating deep learning and machine learning algorithms.

Main Results:

  • The proposed GerdNet-RF model significantly outperforms previous studies in GERD endoscopic classification.
  • Achieved a test accuracy of 92.5% ± 2.1%, an improvement from 78.8% ± 8.5% in prior research.
  • Demonstrated enhanced automated diagnostic capabilities through AI model refinement.

Conclusions:

  • The GerdNet-RF model offers a more accurate and reliable method for GERD diagnosis.
  • AI-enhanced diagnostic tools can improve the assurance of patient healthcare in gastroenterology.
  • This approach signifies a advancement in applying AI for precise endoscopic image analysis in GERD.