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Related Concept Videos

Endoscopic Procedures V: ERCP01:26

Endoscopic Procedures V: ERCP

6.3K
Endoscopic Retrograde Cholangiopancreatography (ERCP) is a diagnostic procedure that combines endoscopy and fluoroscopy to diagnose and treat conditions related to the bile ducts, pancreatic ducts, and gallbladder. This procedure is beneficial for identifying and addressing blockages, gallstones, strictures, and tumors within the biliary or pancreatic systems. ERCP is both diagnostic and therapeutic, offering the ability to visualize and treat identified problems in one session.
Patient...
6.3K

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Evolving techniques to optimize ERCP-based sampling and evaluation of malignant biliary strictures.

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Post-ERCP pancreatitis after self-expanding metal stent placement in patients with pancreatic versus non-pancreatic malignant biliary obstruction.

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Related Experiment Video

Updated: Apr 13, 2026

Laparoscopic Cholecystectomy with Indocyanine Green Fluorescence: Choledochoscopic Stone Extraction and Primary Duct Suture
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Published on: November 25, 2025

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Classification of Biliary Strictures Using Real-Time Cholangioscopy Artificial Intelligence: The SMART-AI Trial.

Neil B Marya1, Patrick D Powers1, Matthew Marcello2

  • 1Division of Gastroenterology, UMass Chan Medical School, Worcester, Massachusetts; Program in Digital Medicine, UMass Chan Medical School, Worcester, Massachusetts.

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
|April 12, 2026
PubMed
Summary

A new artificial intelligence (AI) system for cholangioscopy significantly improves the accuracy of classifying biliary strictures compared to traditional sampling methods and human endoscopists. This AI tool shows promise for better diagnosis of benign versus malignant strictures.

Keywords:
Artificial IntelligenceBiliary Tract MalignancyCholangiocarcinomaCholangioscopyERCP

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Biliary stricture classification is crucial for patient management but often inaccurate with current sampling techniques.
  • Previous studies showed artificial intelligence (AI) analyzing static cholangioscopy images outperformed sampling methods.
  • A need exists for real-time AI tools to improve diagnostic accuracy during procedures.

Purpose of the Study:

  • To prospectively evaluate a real-time cholangioscopy AI system in a single-center trial.
  • To compare the AI's biliary stricture classification performance against standard sampling techniques (brush cytology, forceps biopsy).
  • To assess the AI's accuracy relative to human endoscopists (junior and experienced).

Main Methods:

  • A prospective trial involving 41 patients with suspected biliary strictures.
  • A cholangioscopy AI system analyzed video streams in real-time during procedures.
  • Primary outcome: AI vs. sampling techniques accuracy. Secondary outcome: AI vs. 14 human observers accuracy.

Main Results:

  • The cholangioscopy AI achieved 87.8% accuracy in classifying biliary strictures.
  • AI accuracy (87.8%) was significantly higher than sampling techniques (67.4%; p=0.043).
  • AI outperformed junior (61.5%; p=0.001) and experienced (63.1%; p=0.011) endoscopists.

Conclusions:

  • Traditional sampling techniques and human interpretation of cholangioscopy have limitations in biliary stricture classification.
  • Real-time cholangioscopy AI demonstrates superior accuracy compared to current methods.
  • AI systems may offer significant benefits for aiding biliary stricture diagnosis.