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Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System

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Published on: October 16, 2013

Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter

Benjamin Gutierrez Becker¹, Filippo Arcadu¹, Andreas Thalhammer¹

¹Roche Pharma Research and Early Development Informatics, Roche Innovation Center Basel, Basel, Switzerland.

Therapeutic Advances in Gastrointestinal Endoscopy

|March 15, 2021

Summary

This study introduces an AI system to automatically grade ulcerative colitis severity from colonoscopy videos, improving accuracy and reducing manual annotation. The AI demonstrates robust performance across diverse datasets, paving the way for real-world clinical applications.

Keywords:

artificial intelligence deep learning endoscopy inflammatory bowel disease ulcerative colitis

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Murine Endoscopy for In Vivo Multimodal Imaging of Carcinogenesis and Assessment of Intestinal Wound Healing and Inflammation

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Published on: August 26, 2014

Area of Science:

Gastroenterology
Artificial Intelligence
Medical Imaging

Background:

The Mayo Clinic Endoscopic Subscore is crucial for assessing ulcerative colitis severity.
Current grading methods suffer from significant inter- and intrarater variability.
Machine learning offers potential for standardizing endoscopic assessment.

Purpose of the Study:

To develop and validate a deep learning system for automated Mayo Clinic Endoscopic Subscore grading from raw colonoscopy videos.
To mimic the clinical practice of assessing the entire colonoscopy for severity scoring.
To improve the accuracy and reproducibility of ulcerative colitis endoscopic grading.

Main Methods:

An end-to-end deep learning system was designed to predict a binary Mayo Clinic Endoscopic Subscore directly from colonoscopy videos.
The system processed entire videos, identifying informative regions to compute an overall score.
Training and deployment utilized raw colonoscopy data with colon-section-level ground truth, avoiding manual frame selection.

Main Results:

The system achieved high accuracy in grading endoscopic videos, with Area Under the ROC Curve values of 0.84 (≥1), 0.85 (≥2), and 0.85 (≥3).
Evaluation on 1672 videos from a multisite dataset demonstrated robustness.
The methodology significantly reduced the need for manual annotation.

Conclusions:

Artificial intelligence can accurately and automatically grade ulcerative colitis severity from full endoscopic videos.
Training AI models on diverse, multisite datasets is essential for developing robust systems for real-world deployment.
This AI approach enhances standardization and reproducibility in endoscopic assessment.