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

Burn Epidemiology and Outcome Disparities: A Study of Patients Hospitalized With Burns in Pakistan.

Advances in skin & wound care·2026
Same author

Genital Pyoderma Gangrenosum: A Systematic Review of Reported Cases and Treatment Outcomes.

Journal of cutaneous medicine and surgery·2026
Same author

Benzoyl Peroxide and Malignancy Risk: A Systematic Review and Meta-analysis of Over 4 Million Patients.

Journal of drugs in dermatology : JDD·2026
Same author

Efficacy and Safety of Topical Metformin vs Kligman Formula in the Treatment of Melasma: A Split-Face Study.

Journal of drugs in dermatology : JDD·2026
Same author

Histologic Features of Secondary Syphilis: A Systematic Review and Meta-Analysis.

The American Journal of dermatopathology·2026
Same author

Optical Coherence Tomography for Margin Assessment of Basal Cell Carcinoma in Mohs Micrographic Surgery: A Systematic Review and Meta-Analysis.

Experimental dermatology·2025

Related Experiment Video

Updated: Sep 5, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K

Real-time deep learning assisted skin layer delineation in dermal optical coherence tomography.

Xuan Liu1, Nadiya Chuchvara2, Yuwei Liu1

  • 1Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA.

OSA Continuum
|July 13, 2022
PubMed
Summary

Deep learning with optical coherence tomography (OCT) enables automatic skin layer analysis. This AI-assisted imaging tool accurately measures epidermal thickness and differentiates skin lesions, aiding dermatological diagnosis.

More Related Videos

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
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

Related Experiment Videos

Last Updated: Sep 5, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.5K
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
Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.6K

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical Coherence Tomography (OCT) is a non-invasive imaging technique.
  • Accurate skin layer characterization is crucial for dermatological diagnosis.
  • Manual analysis of OCT images can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a deep learning model for automated skin layer delineation in OCT images.
  • To assess the capability of AI-assisted OCT for quantitative epidermal thickness measurement.
  • To evaluate the potential of this technology for differentiating normal skin from skin lesions.

Main Methods:

  • Acquisition of skin OCT images using a manually scanned single fiber OCT (sfOCT) instrument.
  • Training a U-Net deep learning model for automatic segmentation of skin layers.
  • Quantitative analysis of epidermal thickness and comparison between normal skin and lesions.

Main Results:

  • The U-Net model achieved high accuracy in automatic epidermal thickness estimation.
  • AI-assisted OCT demonstrated clear differentiation between normal skin and various skin lesions.
  • The sfOCT system with AI delineation showed potential for quantitative tissue assessment.

Conclusions:

  • Deep learning-assisted OCT provides accurate and automated skin layer analysis.
  • This technology offers a potential cost-effective tool for clinical dermatology.
  • Applications include diagnosis, tumor margin detection, and objective tissue characterization.