ArticleMayo Clinic proceedings. Digital health2024
An Automated Approach for Diagnosing Allergic Contact Dermatitis Using Deep Learning to Support Democratization of Patch Testing.
Article in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- European Society of Contact Dermatitis Guideline for Diagnostic Patch Testing-Recommendations on Best Practice (Update 2026).Contact dermatitis · 2026Guideline
- AI-Assisted Automated Two-Stage Patch Test Interpretation System Using Vision Transformer.Contact dermatitis · 2026Article
- 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
- Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology.JMIR dermatology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a deep learning algorithm for the analysis of patch testing. Patients and Methods: A retrospective case series between January 1, 2010, and December 31, 2020, was constructed to develop a deep learning model for the classification of patch test results from photographs. The performance of human expert readers reviewing the same photographs blinded to the original clinical physical examination findings was measured to benchmark model performance. Results: Model performance on the independent test set (n=5070 test site locations from 37 patients) achieved an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and an F1 score of 37.1. The optimal cutoff had a sensitivity of 70.1% (136/194; 95% CI, 63.1%-76.5%) and a specificity of 91.7% (4472/4876; 95% CI, 90.9%-92.5%). Conclusion: We demonstrated proof-of-concept utility for detecting allergic contact dermatitis using an automated deep learning approach.
Identifiers
What OpenQuestion holds
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.