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Updated: Nov 7, 2025

Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
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Can a Polyp Detection and Characterization System Predict Complete Resection?

Leon Kliegis1, Wilfried Obst1, Johannes Bruns1

  • 1Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-v.-Guericke University Magdeburg, Magdeburg, Germany.

Digestive Diseases (Basel, Switzerland)
|May 3, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) systems cannot predict complete colon adenoma resection status. Endoscopists should understand AI limitations for accurate interpretation during endoscopic procedures.

Keywords:
Artificial intelligencePolyp detection softwarePolyp resectionPolypectomy

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Area of Science:

  • Gastroenterology
  • Medical technology
  • Artificial Intelligence

Background:

  • Artificial Intelligence (AI) is rapidly advancing in endoscopy.
  • AI systems are being developed for polyp detection and characterization.
  • The study investigates AI's capability in predicting complete resection of colon adenomas.

Purpose of the Study:

  • To evaluate if an AI system can predict complete endoscopic resection of colon adenomas.
  • To assess the accuracy of AI-driven polyp characterization compared to histological findings.

Main Methods:

  • The CAD-Eye AI system was used in 17 consecutive patients undergoing cold snare polypectomy.
  • Post-resection, submucosal space was irrigated, and images were captured by the AI system.
  • AI-derived polyp characterization was compared with the histology of the resected specimens.

Main Results:

  • The AI system provided no information regarding the resection status in any of the 17 cases.
  • The resection plane was consistently classified as adenomatous.
  • All observed adenomas were completely resected, with no potential for misinterpretation in the images.

Conclusions:

  • AI systems trained for polyp characterization in healthy mucosa cannot predict post-resection status.
  • The AI's limitations stem from its training and programming.
  • Endoscopists must understand AI fundamentals and potential interpretation pitfalls.