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Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection

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A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.

John F Eisses¹, Amy W Davis², Akif Burak Tosun³

¹Pediatrics, University of Pittsburgh, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, United States of America.

|October 25, 2014

Summary

A new AI algorithm accurately quantifies pancreatic exocrine mass in injured and recovering models. This method is faster and more precise than manual histology for assessing pancreatic injury and regeneration.

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Area of Science:

Biomedical Engineering
Computational Pathology
Gastroenterology Research

Background:

Assessing exocrine mass changes is crucial for studying pancreatic injury and regeneration models.
Current histological methods for quantifying exocrine content are time-consuming and subjective.

Purpose of the Study:

To develop a rapid and accurate computational method for quantifying pancreatic acinar content using a machine learning algorithm.
To overcome the limitations of manual histological assessment in experimental pancreatitis models.

Main Methods:

A computer-generated learning algorithm was trained using pathologist-defined "ground truth" on HE-stained pancreatic sections from mice undergoing experimental pancreatitis.
The algorithm learned pixel characteristics to differentiate acinar tissue from non-acinar areas.
The validated algorithm was applied to high-resolution images of injured and recovering pancreatic tissue.

Main Results:

The software showed high agreement with the "ground truth" for baseline acinar tissue area (1% ± 0.05% difference).
In injured tissue regions, the software's acinar area quantification differed by only 2.5% ± 0.04% compared to the pathologist.
Morphological analysis revealed the software's superior precision in outlining acini and excluding interstitial space.

Conclusions:

The novel computer learning algorithm provides an accurate and rapid method for quantifying pancreatic acinar content.
This tool has significant potential for researchers and clinicians studying pancreatic injury and regeneration.
The software enhances the precision of exocrine mass assessment in experimental models.