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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

192
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
192
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

82
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
82

You might also read

Related Articles

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

Sort by
Same author

Clinicians' Visual Attention During Suicide Screening Encounters: An Exploratory Eye-Tracking Study.

medRxiv : the preprint server for health sciences·2026
Same author

Exploring Common and Novel Actualized Affordances of Fitbit: Mixed Methods Study.

JMIR human factors·2026
Same author

DUI Detection From Gait Using a Multichannel 1DCNN-Attention-BiLSTM Framework.

IEEE access : practical innovations, open solutions·2025
Same author

Discriminating Between Marijuana and Alcohol Gait Impairments Using Tile CNN With TICA Pooling.

IEEE open journal of engineering in medicine and biology·2025
Same author

Personality-Driven Variations in Fitness App Affordance Actualization Among Adults: Quantitative Survey Study.

JMIR formative research·2025
Same author

Clinical Management of Medication-Assisted Treatment for Opioid Use Disorder Using a Mobile Health App Within a Primary Care Clinic: Quasi-Experimental Study.

JMIR formative research·2025

Related Experiment Video

Updated: Jun 7, 2025

A Simplified Technique for Producing an Ischemic Wound Model
12:00

A Simplified Technique for Producing an Ischemic Wound Model

Published on: May 2, 2012

17.2K

Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers.

Palawat Busaranuvong1, Emmanuel Agu2, Deepak Kumar2

  • 1Data Science DepartmentWorcester Polytechnic Institute Worcester MA 01609 USA.

IEEE Open Journal of Engineering in Medicine and Biology
|November 20, 2024
PubMed
Summary

A new deep learning model, Guided Conditional Diffusion Classifier (ConDiff), accurately detects Diabetic Foot Ulcer (DFU) infections from point-of-care photos. This approach improves diagnostic accuracy, aiding in preventing severe complications.

Keywords:
Diabetic foot ulcersdiffusion modelsdistance-based image classificationgenerative modelswound infection

More Related Videos

Come to the Light Side: In Vivo Monitoring of Pseudomonas aeruginosa Biofilm Infections in Chronic Wounds in a Diabetic Hairless Murine Model
09:15

Come to the Light Side: In Vivo Monitoring of Pseudomonas aeruginosa Biofilm Infections in Chronic Wounds in a Diabetic Hairless Murine Model

Published on: October 10, 2017

13.4K
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

2.8K

Related Experiment Videos

Last Updated: Jun 7, 2025

A Simplified Technique for Producing an Ischemic Wound Model
12:00

A Simplified Technique for Producing an Ischemic Wound Model

Published on: May 2, 2012

17.2K
Come to the Light Side: In Vivo Monitoring of Pseudomonas aeruginosa Biofilm Infections in Chronic Wounds in a Diabetic Hairless Murine Model
09:15

Come to the Light Side: In Vivo Monitoring of Pseudomonas aeruginosa Biofilm Infections in Chronic Wounds in a Diabetic Hairless Murine Model

Published on: October 10, 2017

13.4K
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

2.8K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Diabetic Foot Ulcers (DFUs) pose a significant risk for infections, potentially leading to severe complications and amputations.
  • Accurate and timely detection of DFU infections at the point of care (POC) is crucial for effective patient management and preventing adverse outcomes.

Purpose of the Study:

  • To develop and evaluate a novel deep-learning framework, the Guided Conditional Diffusion Classifier (ConDiff), for accurate detection of DFU infections using POC photographs.
  • To enhance diagnostic performance for DFU infections, thereby reducing the incidence of complications, amputations, and unnecessary healthcare utilization.

Main Methods:

  • The proposed ConDiff framework integrates guided image synthesis with a denoising diffusion model and distance-based classification.
  • It involves generating conditional synthetic images via a reverse diffusion process and classifying infections based on embedding space distances.

Main Results:

  • ConDiff achieved an average accuracy of 81%, outperforming state-of-the-art (SOTA) models by over 3%.
  • The model demonstrated a high sensitivity of 85.4% and improved specificity of 74.4%, surpassing existing SOTA methods.

Conclusions:

  • ConDiff offers a significant advancement in the diagnosis of DFU infections, utilizing generative discriminative models for medical image analysis.
  • This approach shows promise for improving patient outcomes and optimizing clinical decision-making in DFU management.