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Related Experiment Video

Updated: May 8, 2026

Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model

Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model

Published on: February 14, 2021

Deep Learning-Driven Analysis and Quantification of Histopathologic Features in a Dextran Sulfate Sodium-Induced

Arno Doelemeyer¹, Grazyna Wieczorek², Jonas Zierer²

¹Diseases of Aging and Regenerative Medicine, Biomedical Research, Novartis Pharma AG, Basel, Switzerland.

The American Journal of Pathology

|May 7, 2026

Summary

Artificial intelligence (AI) tools can now quantify colitis in mouse models. This digital pathology approach offers a more sensitive and less biased assessment of tissue damage compared to human analysis, aiding drug discovery.

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Investigating Intestinal Inflammation in DSS-induced Model of IBD

Published on: February 1, 2012

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Last Updated: May 8, 2026

Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model

Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model

Published on: February 14, 2021

Using Robotic Systems to Process and Embed Colonic Murine Samples for Histological Analyses

Using Robotic Systems to Process and Embed Colonic Murine Samples for Histological Analyses

Published on: January 7, 2019

Investigating Intestinal Inflammation in DSS-induced Model of IBD

Investigating Intestinal Inflammation in DSS-induced Model of IBD

Published on: February 1, 2012

Area of Science:

Veterinary Pathology
Digital Pathology
Artificial Intelligence in Medicine

Background:

Dextran sodium sulfate (DSS)-induced colitis models are crucial for studying inflammatory bowel disease (IBD) pathophysiology.
Histological scoring of colon sections in these models is vital but often subjective and time-consuming.
Digital pathology offers potential solutions to enhance objectivity and efficiency in histological assessments.

Purpose of the Study:

To develop and validate an AI-based deep learning classifier for quantifying histological features in a DSS-induced colitis mouse model.
To assess the utility of AI in identifying and quantifying tissue damage, cell infiltration, and goblet cell numbers.
To compare the sensitivity and accuracy of AI-driven assessments against traditional pathologist evaluations.

Main Methods:

A deep learning classifier was trained using the HALO image analysis platform on colon sections from a DSS-induced colitis mouse model.
The AI sequentially identified tissue layers (tissues, mucosa, submucosa, lymphoid tissues).
AI quantified tissue damage, cell infiltration, goblet cell counts, and nuclear phenotypes.

Main Results:

AI-based classifiers accurately identified key pathological features and disease progression in the colitis model.
AI assessments demonstrated high correlation and superior sensitivity in detecting histopathological changes compared to pathologist assessments.
The AI approach successfully detected the alleviation of tissue damage.

Conclusions:

Machine learning-based classifiers are efficient for characterizing disease pathology in preclinical models.
AI-driven digital pathology provides a quantitative, sensitive, and unbiased tool for histological assessment.
This technology shows significant utility in the early stages of drug discovery for inflammatory bowel disease.