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A Mouse Ear Model for Allergic Contact Dermatitis Evaluation

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Diagnosing contact dermatitis using machine learning: A review.

Eric McMullen¹, Rajan Grewal¹, Kyle Storm²

¹Division of Dermatology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.

Contact Dermatitis

|June 4, 2024

Summary

Machine learning (ML) shows promise in diagnosing contact dermatitis (CD). While research is limited, ML models can potentially improve diagnostic accuracy and patient outcomes in clinical settings.

Keywords:

contact dermatitis diagnosis machine learning patch testing

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Area of Science:

Dermatology
Medical Informatics
Artificial Intelligence

Background:

Machine learning (ML) presents a significant opportunity to advance contact dermatitis (CD) research.
Integrating ML with comprehensive clinical data may enhance diagnostic capabilities and patch test precision.

Purpose of the Study:

To conduct a comprehensive review of existing literature on the application of ML in contact dermatitis.
To explore the potential of ML in supporting diagnosis and improving patch test accuracy in CD.

Main Methods:

A systematic literature search was performed across Embase, Medline, IEEE Xplore, and ACM Digital Library up to February 7, 2024.
Included studies focused on primary research reporting ML models applied to contact dermatitis.

Main Results:

Six studies met the inclusion criteria after reviewing 7834 identified articles.
ML models were utilized to identify biomarkers, differentiate between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD) using image and clinical data, and predict patch test positivity.
Supervised learning was employed across all models, encompassing a total of 49,704 patients, though accuracy reporting was sparse.

Conclusions:

Despite limited current research, ML demonstrates potential as a supportive tool for clinical diagnosis in contact dermatitis.
Further investigation into the clinical implementation and validation of ML in CD management is warranted.