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Diabetic Foot Ulcer01:31

Diabetic Foot Ulcer

Definition A diabetic foot ulcer (DFU) is a chronic, non-healing wound that develops in individuals with diabetes. It typically occurs on pressure-bearing areas such as the heel, metatarsal heads, or hallux, and carries a high risk of infection and amputation.Pathophysiology • The development of DFUs can be explained by four interconnected mechanisms: neuropathy, ischemia, infection, and impaired wound healing. • Neuropathy is the most common factor. Sensory neuropathy reduces pain perception,...
Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Related Experiment Video

Updated: Jun 19, 2026

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model
08:16

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

An AI-driven framework for diabetic foot ulcer classification, segmentation, and depth estimation.

Saswati Debnath1, Daniel Willson1, Renukadevi M N2

  • 1Centre of excellence-Computer Vision, Alliance School of Advanced Computing, Alliance University, Bengaluru, Karnataka, India.

Scientific Reports
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI framework for assessing Diabetic Foot Ulcers (DFUs). The AI accurately classifies ulcers, segments them, estimates depth, and provides explanations, aiding in better DFU management.

Keywords:
Computer-aided diagnosisDeep learningDiabetic foot ulcerExplainable AIHybrid modelMedical imagingMulti-class classificationTransformer

Related Experiment Videos

Last Updated: Jun 19, 2026

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model
08:16

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Diabetic Foot Ulcers (DFUs) are a severe diabetes complication, often assessed subjectively via visual inspection.
  • Objective and timely DFU assessment is crucial to prevent infection, amputation, and mortality.
  • Current methods lack consistency and quantitative metrics for DFU evaluation.

Purpose of the Study:

  • To develop an automated deep learning framework for comprehensive Diabetic Foot Ulcer (DFU) assessment.
  • To integrate classification, segmentation, relative depth estimation, and explainability for DFU analysis.
  • To provide a research-oriented AI-assisted decision-support tool for DFU management.

Main Methods:

  • Utilized EfficientNet-B0 for binary classification of healthy skin versus DFU.
  • Employed a U-Net with an EfficientNet-B0 encoder for precise ulcer segmentation and morphometric analysis.
  • Integrated explainability techniques (GCAM, LIME) and monocular depth estimation (MiDaS) for topological characterization.
  • Developed an automated reporting module for synthesizing DFU assessment outputs.

Main Results:

  • Achieved strong performance in classifying DFU presence.
  • Demonstrated accurate segmentation of ulcer boundaries for quantitative analysis.
  • Successfully generated meaningful relative depth maps for wound topology.
  • The framework provides structured, clinician-friendly summaries of DFU assessments.

Conclusions:

  • The proposed deep learning framework offers a promising automated approach for DFU assessment.
  • The AI tool integrates multiple analytical capabilities, including classification, segmentation, and depth estimation.
  • Further clinical validation and prospective studies are necessary for real-world deployment in DFU care.