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

You might also read

Related Articles

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

Sort by
Same author

Reducing plastic waste in skin-model research: Sustainable packaging as a practical first step.

The Journal of investigative dermatology·2026
Same author

Implementation of biologic treatments for pyoderma gangrenosum through a service development framework.

Clinical and experimental dermatology·2026
Same author

Health care professionals' confidence in recognising different skin conditions presented in primary care: a cross-sectional survey.

Clinical and experimental dermatology·2026
Same author

Establishing Research Priorities for Artificial Intelligence Approaches in Dermatology Using an e-Delphi Exercise.

Clinical and experimental dermatology·2026
Same author

Translating the Eczema Bathing Study: why context and infection burden matter: reply from authors.

The British journal of dermatology·2026
Same author

Changes in student pharmacists' self-perceived knowledge and confidence regarding suicide prevention as the result of instruction and training.

The mental health clinician·2026

Related Experiment Video

Updated: Aug 29, 2025

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
05:39

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

178

Detecting Eczema Areas in Digital Images: An Impossible Task?

Guillem Hurault1, Kevin Pan1, Ricardo Mokhtari1

  • 1Department of Bioengineering, Imperial College London, London, United Kingdom.

JID Innovations : Skin Science From Molecules to Population Health
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Assessing atopic dermatitis (AD) severity using digital images has poor reliability among dermatologists. This poor agreement in segmenting AD lesions impacts machine learning models for eczema assessment.

Keywords:
AD, atopic dermatitisICC, intraclass correlation coefficientIRR, inter-rater reliabilityKA, Krippendorff’s alphaML, machine learning

More Related Videos

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K

Related Experiment Videos

Last Updated: Aug 29, 2025

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
05:39

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

178
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Traditional atopic dermatitis (AD) severity assessment by clinicians has inter- and intra-rater variability.
  • Telemedicine and machine learning (ML) offer potential for automated AD severity assessment from digital images.
  • Current ML algorithms for AD severity rely on lesion segmentation data from healthcare professionals.

Purpose of the Study:

  • To evaluate the reliability of atopic dermatitis lesion segmentation in digital images.
  • To quantify inter-rater agreement among dermatologists for AD segmentation.
  • To assess the impact of segmentation reliability on ML-based eczema severity assessment.

Main Methods:

  • Four dermatologists independently segmented AD lesions in 80 digital images from a clinical trial.
  • Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC) at pixel and area levels.
  • ICC was assessed across different image resolutions.

Main Results:

  • The average ICC for AD segmentation was 0.45 (SE=0.04), indicating poor agreement between raters.
  • Inter-rater reliability varied significantly across different images.
  • AD lesion segmentation in digital images is highly dependent on the individual dermatologist.

Conclusions:

  • The poor reliability of AD segmentation among dermatologists poses a significant limitation for training ML algorithms.
  • Data used for training ML models for eczema severity assessment must account for segmentation variability.
  • Improving standardization or consensus in AD lesion delineation is crucial for reliable automated assessment.