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Diabetes Mellitus: Overview and Type I Subtype

Diabetes Mellitus: Overview and Type I Subtype

Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...

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Updated: May 8, 2025

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes.

Sanghoon Kang¹, Jesus D Penaloza Aponte^2,3, Omar Elashkar¹

¹Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, Intelligent Critical Care Center, College of Pharmacy, University of Florida, Orlando, FL, USA.

Journal of Pathology Informatics

|December 25, 2024

Summary

This summary is machine-generated.

This study developed an AI-powered workflow to analyze pancreatic islet cell heterogeneity in type 1 diabetes (T1D) using whole slide images. The tool enhances understanding of T1D pathogenesis by quantifying endocrine cell composition and inflammation.

Keywords:

Digital pathology Islet heterogeneity Machine learning Type 1 diabetes Whole slide images

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Last Updated: May 8, 2025

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Published on: March 4, 2011

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Published on: March 9, 2022

Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets

Leprdb Mouse Model of Type 2 Diabetes: Pancreatic Islet Isolation and Live-cell 2-Photon Imaging Of Intact Islets

Published on: May 11, 2015

Area of Science:

* Endocrinology and Diabetes Research
* Computational Pathology
* Immunohistochemistry and Digital Pathology

Background:

* Human islets exhibit significant heterogeneity in size, cell composition, and architecture.
* The Network for Pancreatic Organ donors with Diabetes (nPOD) program provides extensive immunohistochemistry-stained whole slide images (WSIs).
* Understanding islet heterogeneity is crucial for deciphering the natural history and pathogenesis of type 1 diabetes (T1D).

Purpose of the Study:

* To develop an enhanced machine learning-assisted workflow for analyzing WSI data from the nPOD resource.
* To quantify endocrine cell heterogeneity and T-cell infiltration in human islets from donors with and without T1D.
* To create a user-friendly, open-source tool for high-throughput analysis of islet pathology.

Main Methods:

* Utilized QuPath software as the primary interface for WSI analysis.
* Employed the Segment Anything Model (SAM) for precise cell boundary segmentation and an artificial neural network-based pixel classifier for endocrine cell segmentation.
* Developed a script to quantify CD3+ T-cell infiltration within and around islets, identifying inflamed islets.

Main Results:

* The workflow achieved an average quality score of 0.91 per slide for cell boundary definition using SAM.
* The pixel classifier accurately segmented insulin- and glucagon-stained cytoplasmic regions.
* The developed methods enable rapid, high-throughput quantification of endocrine cells and islet inflammation.

Conclusions:

* An open-source, machine learning-assisted workflow was successfully developed for analyzing islet heterogeneity in WSIs.
* This workflow facilitates rapid and high-throughput determination of endocrine cell characteristics and inflammation.
* The tool is expected to accelerate research into T1D endotypes and pathogenesis by providing deeper insights into islet heterogeneity.