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Related Concept Videos

Peptic Ulcer Disease III: Clinical Manifestations and Diagnostic Studies01:28

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Peptic ulcer disease (PUD) presents with diverse symptoms depending on the location and severity of the ulcer. Clinical manifestations of peptic ulcer include dull pain and a burning sensation in the mid-epigastric region.
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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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Endoscopic Procedures V: ERCP01:26

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Related Experiment Video

Updated: May 21, 2025

Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System
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Enhancing diagnostics: ChatGPT-4 performance in ulcerative colitis endoscopic assessment.

Asaf Levartovsky1, Ahmad Albshesh1, Ana Grinman1

  • 1Gastroenterology, affiliated with Tel Aviv University, Sheba Medical Center, Tel Hashomer, Israel.

Endoscopy International Open
|March 20, 2025
PubMed
Summary

ChatGPT-4 demonstrates potential in grading ulcerative colitis endoscopic images using the Mayo Endoscopic Subscore, achieving accuracy comparable to inflammatory bowel disease specialists.

Keywords:
Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...)Endoscopy Lower GI TractImage and data processing, documentatitonInflammatory bowel diseaseQuality and logistical aspects

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Area of Science:

  • Gastroenterology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • The Mayo Endoscopic Subscore (MES) is crucial for assessing ulcerative colitis (UC) activity.
  • Artificial intelligence (AI) offers potential to improve diagnostic accuracy and reduce interobserver variability in endoscopy.
  • This study investigates the utility of ChatGPT-4 for MES grading in UC.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of ChatGPT-4 in grading endoscopic images of UC patients using the MES.
  • To compare ChatGPT-4's performance against expert consensus and inflammatory bowel disease (IBD) specialists.

Main Methods:

  • Expert consensus graded real-world UC endoscopic images using the MES.
  • Uniformly graded images were assessed by three IBD specialists and ChatGPT-4.
  • ChatGPT-4 and IBD specialist gradings were compared to the expert consensus.

Main Results:

  • Thirty of 50 images achieved uniform expert grading.
  • ChatGPT-4 achieved a mean accuracy of 78.9% compared to the consensus board.
  • IBD specialists had a mean accuracy of 81.1%, with no significant difference from ChatGPT-4 (P=0.71).

Conclusions:

  • ChatGPT-4 shows potential for assessing UC mucosal inflammation severity from endoscopic images.
  • The AI model performed comparably to IBD specialists without specific fine-tuning.
  • This suggests AI's role in supporting endoscopic assessment in UC.