ArticleOSA continuum2021
Real-time deep learning assisted skin layer delineation in dermal optical coherence tomography.
Article in OSA continuum, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Assessing Plantar Skin Epidermal Layer in Response to Various Insole Hardness via Deep Learning Enabled Optical Coherence Tomography Analysis.Journal of biophotonics · 2026Article
- OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.Optics letters · 2025Article
- Segmentation of 3D OCT Images of Human Skin Using Neural Networks with U-Net Architecture.Sovremennye tekhnologii v meditsine · 2025Article
- LS-Net: lightweight segmentation network for dermatological epidermal segmentation in optical coherence tomography imaging.Biomedical optics express · 2024Article
- Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications: LC-OCT reveals new biological markers of AD.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms to identify quantitative biomarkers of facial skin ageing.Scientific reports · 2023Article
- Manually scanned single fiber optical coherence tomography for skin cancer characterization.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
We present deep learning assisted optical coherence tomography (OCT) imaging for quantitative tissue characterization and differentiation in dermatology. We utilize a manually scanned single fiber OCT (sfOCT) instrument to acquire OCT images from the skin. The focus of this study is to train a U-Net for automatic skin layer delineation. We demonstrate that U-Net allows quantitative assessment of epidermal thickness automatically. U-Net segmentation achieves high accuracy for epidermal thickness estimation for normal skin and leads to a clear differentiation between normal skin and skin lesions. Our results suggest that a single fiber OCT instrument with AI assisted skin delineation capability has the potential to become a cost-effective tool in clinical dermatology, for diagnosis and tumor margin detection.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.