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Related Concept Videos

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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Deep Learning Classification for Diabetic Foot Thermograms.

Israel Cruz-Vega1, Daniel Hernandez-Contreras2, Hayde Peregrina-Barreto3

  • 1CONACYT Research Fellow-National Institute of Astrophysics, Optics, and Electronics, Santa Maria Tonantzintla, Puebla 72840, Mexico.

Sensors (Basel, Switzerland)
|April 3, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning (DL) can classify diabetic foot thermograms to detect risks of ulceration. This study introduces a novel DL structure that achieves high accuracy, aiding in early medical diagnosis for Diabetes Mellitus (DM).

Keywords:
artificial neural networksdeep learningdiabetes mellitusdiabetic footsupport vector machinethermography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Diabetology

Background:

  • Diabetes Mellitus (DM) is a prevalent global disease with significant mortality.
  • Diabetic foot complications, including plantar ulcers leading to amputation, are a major concern.
  • Thermography can detect plantar temperature changes indicative of ulceration risk, but patterns are inconsistent in diabetic patients.

Purpose of the Study:

  • To analyze the effectiveness of Artificial Intelligence (AI) and Deep Learning (DL) for classifying diabetic foot thermograms.
  • To compare traditional machine learning techniques with novel DL structures for improved detection of abnormal plantar temperature changes.
  • To highlight the advantages and limitations of AI and DL in diagnosing diabetic foot complications.

Main Methods:

  • Comparison of common transfer learning models (AlexNet, GoogleNet) with a newly designed DL structure trained from scratch.
  • Application of AI and DL techniques to classify thermograms from Diabetes Mellitus (DM) patients and control groups.
  • Multi-level classification based on a thermal change index for risk stratification.

Main Results:

  • The novel DL structure achieved higher accuracy and quality measures compared to existing models.
  • AI and DL demonstrated significant utility in classifying diabetic foot thermograms.
  • High accuracy in classification indicates the potential of these AI tools in medical diagnosis.

Conclusions:

  • AI and DL, particularly novel DL networks, are effective auxiliary tools for the early detection and diagnosis of diabetic foot complications.
  • This study presents the first application of DL networks for classifying diabetic foot thermograms, showing promising results.
  • The developed methods can aid clinicians in identifying patients at higher risk of developing plantar ulcers.