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

Corrigendum to "Visual modalities-based multimodal fusion for surgical phase recognition" [Comput. Biol. Med. 166 (2023) 107453].

Computers in biology and medicine·2026
Same author

Estimating Economic Burden of Cancer in South Korea in 2015 to 2019.

Value in health regional issues·2026
Same author

Synergistic Induction of Neutrophilic Inflammatory Programs by <i>Staphylococcus aureus</i> and Cigarette Smoke in Airway Epithelial Cells.

Immune network·2026
Same author

Selective and direct hydrogen generation from mixed plastic waste via alkaline thermal treatment with inherent carbon storage.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Estimation of VOC emissions from an electric vehicle interior as a function of cabin air temperature using a selected ion tube flow mass spectrometer (SIFT-MS) measurement.

Chemosphere·2026
Same author

Editable and Applicable Indoor Scene Rearrangement via Dynamic Programming.

IEEE transactions on visualization and computer graphics·2026

Related Experiment Video

Updated: Oct 20, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

547

Optimization of psoriasis assessment system based on patch images.

Cho-I Moon1, Jiwon Lee1, HyunJong Yoo2

  • 1Department of Software Convergence, Graduate School, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City, Chungnam-do, 31538, Republic of Korea.

Scientific Reports
|September 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces advanced algorithms and a new dataset to objectively evaluate psoriasis severity. The developed system improves diagnostic accuracy for localized psoriasis, moving beyond subjective clinical assessments.

More Related Videos

The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis
11:39

The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis

Published on: July 11, 2013

39.1K
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

227

Related Experiment Videos

Last Updated: Oct 20, 2025

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

547
The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis
11:39

The Goeckerman Regimen for the Treatment of Moderate to Severe Psoriasis

Published on: July 11, 2013

39.1K
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

227

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Psoriasis is a chronic inflammatory skin condition linked to comorbidities like heart disease and diabetes.
  • Current psoriasis severity assessment relies on the subjective Psoriasis Area and Severity Index (PASI) score, leading to evaluation deviations.
  • Objective and accurate methods are needed for psoriasis severity evaluation in clinical research and practice.

Purpose of the Study:

  • To develop optimal algorithms for psoriasis lesion segmentation and severity classification.
  • To create a new dataset of psoriasis patch images for improved model training and generalization.
  • To establish a quantitative system for evaluating psoriasis severity and a novel PASI scoring method.

Main Methods:

  • Development of algorithms for effective segmentation of psoriasis lesion areas.
  • Classification of psoriasis severity using machine learning models.
  • Creation of a new dataset comprising erythema and scaling patch images.
  • Evaluation of segmentation and classification models to identify the best-performing techniques.
  • Analysis of image features correlated with psoriasis severity.

Main Results:

  • Optimal algorithms were identified for psoriasis lesion segmentation and severity classification.
  • A new psoriasis dataset was constructed, enhancing diagnostic generalization performance.
  • The proposed system offers improved accuracy in evaluating localized psoriasis severity.
  • A quantitative PASI scoring method was developed, reducing subjective assessment variability.

Conclusions:

  • The study presents optimal techniques for objective psoriasis severity evaluation.
  • The newly developed dataset and algorithms enhance psoriasis diagnosis and evaluation accuracy.
  • The proposed system provides a more precise and quantitative method for assessing psoriasis severity.
  • This quantitative approach aids in better management and clinical trial evaluation of psoriasis.