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Related Concept Videos

Diabetic Foot Ulcer01:31

Diabetic Foot Ulcer

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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...
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Diabetic Retinopathy01:27

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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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Diabetic Neuropathy01:22

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DefinitionDiabetic neuropathy is nerve damage caused by long-standing diabetes mellitus. It results directly from prolonged high blood sugar levels.PathophysiologyThe pathophysiology of diabetic neuropathy involves both metabolic and vascular disturbances triggered by chronic hyperglycemia.Metabolic injury: Elevated glucose levels activate the polyol pathway within nerve cells, leading to the accumulation of sorbitol and fructose. This increases oxidative stress, disrupts normal nerve...
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Enhancing Early Detection of Diabetic Foot Ulcers Using Deep Neural Networks.

A Sharaf Eldin1,2, Asmaa S Ahmoud3, Hanaa M Hamza4

  • 1Department of Information and Decision Support Systems, Faculty of Information Technology and Computer Science, Sinai University, Arish 16020, Egypt.

Diagnostics (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new hybrid AI model for early diabetic foot ulcer (DFU) detection using thermal imaging. The model achieves high accuracy and real-time performance, outperforming existing methods for clinical use.

Keywords:
deep learning (DL)deep neural network (DNN)diabetic footdiabetic foot ulcers (DFUs)plantar thermogramsthermal images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Diabetic foot ulcers (DFUs) are a severe diabetes complication, often leading to amputation if not detected early.
  • Current DFU detection methods face challenges in computational complexity, generalizability, and diagnostic speed.
  • There is a need for advanced, efficient, and accurate computer-aided detection (CAD) systems for DFUs.

Purpose of the Study:

  • To develop a novel hybrid diagnostic framework for early, real-time computer-aided detection (CAD) of diabetic foot ulcers (DFUs).
  • To improve the diagnostic performance and clinical applicability of DFU detection systems.
  • To integrate traditional feature extraction with deep learning for enhanced DFU identification.

Main Methods:

  • Utilized plantar thermograms to identify thermal asymmetries indicative of early DFUs.
  • Combined Oriented FAST and Rotated BRIEF (ORB) with Bag of Features (BOF) for handcrafted feature extraction.
  • Integrated deep features from Convolutional Neural Networks (ResNet50, AlexNet, EfficientNet) and fused them with handcrafted features.
  • Employed a lightweight deep neural network (DNN) for binary classification.

Main Results:

  • Achieved high diagnostic performance: 98.51% accuracy, 100% precision, 98.98% sensitivity, and 1.00 AUC on a dataset of 1670 images.
  • Demonstrated the superiority of the hybrid ORB + DL approach over standalone methods through ablation studies.
  • Outperformed state-of-the-art models (DFU_VIRNet, DFU_QUTNet) in accuracy and AUC while maintaining real-time capability and lower computational cost.

Conclusions:

  • The study presents the first hybrid framework integrating ORB handcrafted features with deep neural representations for DFU detection from thermal images.
  • The proposed model offers high accuracy, robustness, and real-time performance, surpassing existing methods.
  • The framework shows significant potential for effective clinical deployment in early DFU diagnosis.