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Pathophysiology of Diabetes

Pathophysiology of Diabetes

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...

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Updated: Aug 13, 2025

A Simplified Technique for Producing an Ischemic Wound Model

A Simplified Technique for Producing an Ischemic Wound Model

Published on: May 2, 2012

Diabetic wounds and artificial intelligence: A mini-review.

Samabia Tehsin¹, Sumaira Kausar², Amina Jameel³

¹Computer Science, Bahria University, Karachi 75260, Sindh, Pakistan. samabiatehsin.bukc@bahria.edu.pk.

World Journal of Clinical Cases

|January 23, 2023

Summary

Artificial intelligence (AI) offers new ways to diagnose and treat diabetic wounds, improving patient outcomes. This review explores AI

Keywords:

Amputation Artificial intelligence Diabetic wounds Diagnosis Foot ulcer Machine learning

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Last Updated: Aug 13, 2025

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Protocol to Create Chronic Wounds in Diabetic Mice

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Area of Science:

Computational science
Medical science
Artificial intelligence

Background:

Diabetic wounds, particularly foot ulcers, exhibit delayed healing due to vascular complications, increasing infection and amputation risks.
Effective management of diabetic wounds is crucial for improving patient quality of life.

Purpose of the Study:

To review diagnostic and treatment research for diabetic wound healing utilizing artificial intelligence (AI) and computational science.
To present a framework for diabetic wound assessment using AI.
To highlight AI's current and potential contributions to diabetic wound patient care.

Main Methods:

Review of existing research on AI and computational science applications in diabetic wound healing.
Development of a framework for AI-driven diabetic wound assessment.

Main Results:

AI demonstrates significant potential in enhancing the diagnosis, prognosis, and treatment of diabetic wounds.
A framework for AI-based diabetic wound assessment has been proposed.

Conclusions:

AI integration in medical science can significantly improve the management of diabetic wounds.
Future research directions aim to further leverage AI for enhanced clinician and patient support in diabetic wound care.