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Medical Sciences
Volume 14
Issue 2
10.3390/medsci14020322
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Review

A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation

by
Dominyka Stragyte
1,*,
Gvidas Mikalauskas
2,
Katrina Gaidulevic
2,
Renata Paukstaitiene
3,
Kestutis Stasaitis
4,
Vidas Raudonis
5 and
Skaidra Valiukeviciene
1
1
Faculty of Medicine, Medical Academy, Lithuanian University of Health Sciences, LT-44307 Kaunas, Lithuania
2
Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, LT-44307 Kaunas, Lithuania
3
Department of Physics, Mathematics and Biophysics, Lithuanian University of Health Sciences, LT-44307 Kaunas, Lithuania
4
Department of Emergency Medicine, Lithuanian University of Health Sciences, LT-44307 Kaunas, Lithuania
5
Automation Department, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Med. Sci. 2026, 14(2), 322; https://doi.org/10.3390/medsci14020322 (registering DOI)
Submission received: 10 April 2026 / Revised: 26 May 2026 / Accepted: 12 June 2026 / Published: 15 June 2026
Versions Notes

Abstract

Background: Allergic contact dermatitis (ACD) is a common inflammatory skin disease and patch testing (PT) remains the gold standard for its diagnosis; however, PT interpretation is time-consuming and prone to inter-observer variability. Growing advances in digital imaging and artificial intelligence (AI) have encouraged the development of automated PT evaluation systems. This review aimed to summarize the use of deep learning networks (DNNs) and image-preprocessing techniques for PT classification. Methods: A literature review was conducted to identify original research published between 2020 and 2025 that applied deep learning algorithms to PT image analysis. Included studies were assessed with respect to model architecture, dataset characteristics, preprocessing strategies, and diagnostic performance. Results: Six original studies employing deep learning for PT image classification met the inclusion criteria. They employed a range of architectures, including YOLOv5x, EfficientNetB0, Xception, and custom CNN models. Reported diagnostic performance varied, with accuracy values ranging from 90% to 99.5%, F1-scores from 0.37 to 0.98, and AUROC values up to 0.94. Despite promising results, models remain unreliable for ICDRG grading, especially for severe reactions, and methodological variability in dataset composition, imaging conditions, preprocessing pipelines, and classification tasks limits comparability across studies. Conclusions: Deep learning shows promise for automated PT interpretation, but further standardized and multicenter studies with detailed preprocessing protocols and comprehensive ICDRG grading are required for clinical implementation.
Keywords: patch test; allergic contact dermatitis; contact dermatitis; deep learning; convolutional neural network; artificial intelligence; machine learning patch test; allergic contact dermatitis; contact dermatitis; deep learning; convolutional neural network; artificial intelligence; machine learning

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MDPI and ACS Style

Stragyte, D.; Mikalauskas, G.; Gaidulevic, K.; Paukstaitiene, R.; Stasaitis, K.; Raudonis, V.; Valiukeviciene, S. A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation. Med. Sci. 2026, 14, 322. https://doi.org/10.3390/medsci14020322

AMA Style

Stragyte D, Mikalauskas G, Gaidulevic K, Paukstaitiene R, Stasaitis K, Raudonis V, Valiukeviciene S. A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation. Medical Sciences. 2026; 14(2):322. https://doi.org/10.3390/medsci14020322

Chicago/Turabian Style

Stragyte, Dominyka, Gvidas Mikalauskas, Katrina Gaidulevic, Renata Paukstaitiene, Kestutis Stasaitis, Vidas Raudonis, and Skaidra Valiukeviciene. 2026. "A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation" Medical Sciences 14, no. 2: 322. https://doi.org/10.3390/medsci14020322

APA Style

Stragyte, D., Mikalauskas, G., Gaidulevic, K., Paukstaitiene, R., Stasaitis, K., Raudonis, V., & Valiukeviciene, S. (2026). A Structured Review of Deep Learning Approaches and Image-Preprocessing Techniques for Automated Contact Allergy Patch Test Interpretation. Medical Sciences, 14(2), 322. https://doi.org/10.3390/medsci14020322

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