ArticleJID innovations : skin science from molecules to population health2022
Detecting Eczema Areas in Digital Images: An Impossible Task?
Article in JID innovations : skin science from molecules to population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review.BMC medical informatics and decision making · 2025Pooled it
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging.Scientific reports · 2025Article
- Reliable Detection of Eczema Areas for Fully Automated Assessment of Eczema Severity from Digital Camera Images.JID innovations : skin science from molecules to population health · 2023Article
- Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential.JID innovations : skin science from molecules to population health · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (segment) AD lesions before assessing lesional severity and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images. Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intraclass correlation coefficient at the pixel and the area levels for different resolutions of the images. The average intraclass correlation coefficient was 0.45 (
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Registered trials
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