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Related Concept Videos

Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy01:26

Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy

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Sigmoidoscopy and laparoscopy are distinct medical procedures that enable physicians to internally inspect different parts of the GI tract. Although they serve different purposes, each is essential for diagnosing and, in some cases, treating various medical conditions.
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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.
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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Endoscopic Procedures II: Colonoscopy01:25

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The colon, or large intestine, is the final segment of the digestive system. Its primary functions include absorbing water and vitamins produced by gut bacteria and transforming waste from liquid to solid to form stool. In adults, the large intestine is approximately 5 feet long and consists of four main sections:
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Machine learning in gastrointestinal endoscopy: challenges and opportunities.

Sergejs Lobanovs1,2, Jekaterina Aleksejeva3,2, Alise Kitija Rūtiņa3,2

  • 1Faculty of Medicine, University of Latvia, Riga, Latvia sergeyslobanovs@gmail.com.

BMJ Open Gastroenterology
|October 3, 2025
PubMed
Summary

Machine learning (ML) enhances gastrointestinal endoscopy for disease diagnosis and progression prediction. Challenges include data limitations, generalizability, and ethical concerns, requiring careful integration for improved patient care.

Keywords:
DIAGNOSTIC AND THERAPEUTIC ENDOSCOPYENDOSCOPIC PROCEDURESENDOSCOPY

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Area of Science:

  • Gastroenterology
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Classical endoscopy relies on direct visualization for gastrointestinal disease diagnosis and treatment.
  • Machine learning (ML) integration offers advanced capabilities for endoscopic examinations.
  • ML enhances diagnostic precision, abnormality detection, pathology classification, and disease progression prediction.

Purpose of the Study:

  • To provide a comprehensive structural analysis of ML approaches in endoscopy.
  • To explore current ML applications for improving diagnostic accuracy and procedural workflows.
  • To examine technical and ethical barriers to ML adoption in gastroenterology.

Main Methods:

  • Overview of classical endoscopic methodology.
  • Exploration of current ML applications in endoscopy.
  • Analysis of technical limitations and ethical considerations.

Main Results:

  • ML enhances diagnostic precision and workflow efficiency in endoscopy.
  • Effectiveness of ML models is constrained by training data quality and diversity.
  • Technical and ethical barriers hinder widespread ML adoption in clinical settings.

Conclusions:

  • Responsible ML deployment requires addressing patient privacy, data security, and algorithmic bias.
  • Overcoming barriers is crucial for developing reliable ML-driven endoscopic practices.
  • ML integration holds significant potential to enhance disease detection and patient care in gastroenterology.