Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Peripheral Artery Disease IV: Nursing Management01:26

Peripheral Artery Disease IV: Nursing Management

314
 The nursing management of a patient with peripheral artery disease (PAD) begins with a thorough assessment of the patient’s health history and clinical manifestations.AssessmentHealth History: Evaluate the patient’s history of hypertension, hyperlipidemia, family history of cardiovascular issues, and lifestyle factors such as dietary patterns, smoking, and physical activity.Physical Examination:Assess the affected extremity for decreased or absent peripheral pulses,...
314
Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

317
Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
317

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explainable Lightweight AI for the Identification of Right-Sided Cardiac Dysfunction in a Saudi Arabian Diabetic Cohort.

Journal of clinical medicine·2026
Same author

An Interpretable Hybrid SFNet Deep Learning Framework for Multi-Site Bone Fracture Detection in Medical Imaging.

Diagnostics (Basel, Switzerland)·2026
Same author

Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.

Diagnostics (Basel, Switzerland)·2026
Same author

Multimodal image fusion for enhanced vehicle identification in intelligent transport.

PeerJ. Computer science·2025
Same author

Neuroimaging and Machine Learning in OCD: Advances in Diagnostic and Therapeutic Insights.

Brain sciences·2025
Same author

Multimodal scene recognition using semantic segmentation and deep learning integration.

PeerJ. Computer science·2025

Related Experiment Video

Updated: Jan 7, 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

3.9K

Advances in Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic

Haifa F Alhasson1, Shuaa S Alharbi1

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.

Biomedicines
|December 30, 2025
PubMed
Summary

Machine learning (ML) and deep learning (DL) show promise for diagnosing diabetic foot ulcers (DFUs) using images. Improved data sharing is crucial for enhancing accuracy and patient care.

Keywords:
convolutional neural networksdeep learningdfu datasetdiabetes mellitusdiabetic foot ulcersmachine learningthermogram

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers
04:09

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Published on: March 3, 2023

3.6K

Related Experiment Videos

Last Updated: Jan 7, 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

3.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers
04:09

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Published on: March 3, 2023

3.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Diabetic Foot Ulcer Diagnosis

Background:

  • Diabetic foot ulcers (DFUs) represent a significant complication of diabetes, necessitating advanced diagnostic tools.
  • Current diagnostic methods for DFUs can be subjective and time-consuming.
  • The integration of machine learning (ML) and deep learning (DL) offers potential for objective and efficient DFU diagnosis.

Purpose of the Study:

  • To systematically review machine learning (ML) and deep learning (DL) applications in image-based diabetic foot ulcer (DFU) diagnosis.
  • To identify trends, challenges, and quality metrics in ML/DL research for DFU detection, segmentation, and classification.
  • To assess the effectiveness and limitations of current ML/DL approaches in DFU diagnosis.

Main Methods:

  • A comprehensive systematic literature search was performed across 14 databases for studies published between 2010 and 2025.
  • Inclusion criteria focused on ML/DL image-based studies for DFU diagnosis with quantitative outcomes.
  • 102 studies were selected from an initial pool of 4653 articles after detailed review.

Main Results:

  • ML/DL models demonstrated high diagnostic performance for DFUs, with accuracy ranging from 0.88 to 0.97.
  • Commonly utilized ML techniques included Support Vector Machines (SVMs), while U-Net and fully convolutional neural networks (FCNNs) were prevalent in DL.
  • Thermal infrared imaging emerged as a promising modality, but limited public accessibility of datasets (45% for segmentation, 67.3% for classification) hinders reproducibility.

Conclusions:

  • ML and DL are effective tools for DFU diagnosis, offering high accuracy and sensitivity.
  • The review underscores the critical need for increased data availability and sharing to bolster the reproducibility and reliability of ML/DL models.
  • Enhancing data accessibility will ultimately contribute to improved diagnostic accuracy and better patient outcomes in DFU management.