ArticleContact dermatitis2025
Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Erythema in a Patch Test.
Article in Contact dermatitis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Quantitative Evaluation of Patch Test Reactions Using a 3D Camera-Derived Features and Machine Learning: The Role of Temporal Dynamics.Bioengineering (Basel, Switzerland) · 2026Article
- A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation.Medical sciences (Basel, Switzerland) · 2026Review
- Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Erythema in a Patch Test.Contact dermatitis · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe patch test evaluates skin erythema, infiltration, papules and vesicles following exposure to various substances, including metals, cosmetics and medicines. Accurate evaluation of these conditions requires consistent skin score assessments, precise visual grading and minimal inter-expert variability.
objectivesThis study aimed to develop a skin irritation artificial intelligence model based on the YOLOv5x object detection framework to automatically detect skin irritation from the patch test images for multiple test substances.
methodsPatch test images were collected with test sites marked to enable the YOLOv5x algorithm to locate the samples. An expert assigned a score to each sample (0-4) for training and validation. The model was trained using 83 629 data points. Evaluation and validation were performed with 1312 and 1536 data points, respectively.
resultsThe model achieved an overall accuracy of 0.983 at both 24 and 48 h, with an F1 score (harmonic mean of recall and precision) of 0.982. The areas under the curve (AUCs) for scores 0, 1 and 2 were 0.914, 0.838 and 0.865, respectively. The sensitivity for a score of 0 was 0.997.
conclusionThese findings suggest that this AI model effectively supports and classifies skin irritation, thereby facilitating faster and more accurate dermatological evaluations.
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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.