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Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and
Ioannis A Vezakis1, George I Lambrou1,2,3, Aikaterini Kyritsi4
1Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece.
Bioengineering (Basel, Switzerland)
|August 26, 2023
Summary
Deep learning accurately identifies allergens causing Allergic Contact Dermatitis (ACD) using multi-modal skin images. This AI approach enhances diagnostic speed and precision, reducing clinician workload.
Area of Science:
- Dermatology
- Artificial Intelligence
- Medical Imaging
Background:
- Allergic Contact Dermatitis (ACD) is diagnosed via epicutaneous patch testing, the current gold standard.
- Patch testing is limited by observer bias and high resource demands.
- Deep learning offers potential for automating ACD allergen identification.
Purpose of the Study:
- To investigate the feasibility of a deep learning classifier for automated ACD allergen identification.
- To develop and evaluate a deep learning approach using multi-modal skin imaging data.
- To improve diagnostic accuracy and efficiency in ACD detection.
Main Methods:
- Collected a dataset of 1579 multi-modal skin images from 200 patients using an Antera 3D® camera.
- Developed a deep learning classifier incorporating a context-retaining pre-processing technique.
- Utilized a combination of color images and hemoglobin concentration maps for analysis.
Main Results:
- The deep learning approach achieved high diagnostic performance.
- Demonstrated potential for over 86% recall and 94% specificity in identifying skin reactions.
- The combined use of color and hemoglobin data improved diagnostic accuracy.
Conclusions:
- Deep learning shows promise for automating ACD allergen identification.
- The proposed method can lead to faster, more accurate ACD diagnosis.
- This AI-driven approach can significantly reduce clinician workload.
