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Related Concept Videos

Endoscopic Procedures II: Colonoscopy01:25

Endoscopic Procedures II: Colonoscopy

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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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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
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Endoscopic Procedures III: Video Capsule Endoscopy01:28

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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 IV: Sigmoidoscopy and Laproscopy01:26

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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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Assessment of the Rectum and Anus01:25

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Evaluating the rectum and anus plays a crucial role in conducting a thorough physical examination of the gastrointestinal system. Although it may be uncomfortable and often embarrassing for the patient, it holds immense diagnostic value, particularly in detecting gastrointestinal diseases and abnormalities. This guide will explain how to perform this assessment using inspection and palpation methods.
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Anatomy of the Intestines01:23

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Although digestion of proteins, carbohydrates, and lipids may begin in the stomach, it is completed in the intestine. The absorption of nutrients, water, and electrolytes from food and drink also occurs in the intestine. The intestines can be divided into two structurally distinct organs—the small and large intestines.
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Related Experiment Video

Updated: Sep 28, 2025

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists
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The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.

Shorabuddin Syed1, Adam Jackson Angel2, Hafsa Bareen Syeda3

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, USA.

Biomedical Engineering Systems and Technologies, International Joint Conference, BIOSTEC ... Revised Selected Papers. BIOSTEC (Conference)
|April 4, 2022
PubMed
Summary

This study introduces a novel method using combined clinical embeddings to extract key information from colonoscopy, pathology, and radiology reports, improving colorectal cancer research data.

Keywords:
Clinical Concept ExtractionColonoscopyDeep LearningNatural Language ProcessingWord Embeddings

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Colonoscopy is vital for detecting colorectal cancer, with quality metrics like adenoma detection rates crucial for patient outcomes.
  • Current quality metrics are fragmented across colonoscopy, pathology, and radiology reports, hindering comprehensive colorectal cancer research.
  • Manual data abstraction from these disparate reports is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and evaluate a Natural Language Processing (NLP) approach for extracting comprehensive clinical concepts from consolidated colonoscopy-related documents.
  • To improve the integration and standardization of documentation for enhanced colorectal cancer research.

Main Methods:

  • Utilized Natural Language Processing (NLP) and Machine Learning (ML) techniques, specifically contextual word embedding models like BERT and FLAIR.
  • Trained BERT and FLAIR embeddings on unlabeled colonoscopy documents.
  • Developed a hybrid Artificial Neural Network (h-ANN) to concatenate and fine-tune BERT and FLAIR embeddings.
  • Fine-tuned three models initialized from the h-ANN using annotated corpora from colonoscopy, pathology, and radiology reports.

Main Results:

  • Achieved high F1-scores for concept extraction: 91.76% for colonoscopy reports, 92.25% for pathology reports, and 88.55% for radiology reports.
  • Demonstrated the effectiveness of concatenated clinical embeddings in improving NLP performance for clinical text.
  • Successfully extracted comprehensive clinical concepts from disparate report types.

Conclusions:

  • Concatenated clinical embeddings offer a powerful method for integrating and standardizing clinical data from multiple sources.
  • This NLP-driven approach significantly enhances the extraction of quality metrics for colorectal cancer research.
  • The developed models show promise for improving the efficiency and accuracy of clinical data abstraction.