Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance
Epicutaneous patch testing is a well-established diagnostic method for identifying substances that may cause Allergic Contact Dermatitis (ACD), a common skin condition caused by exposure to environmental allergens. While the patch test remains the gold standard for identifying allergens, it is prone...
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| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/8/924 |
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| author | Ioannis A. Vezakis George I. Lambrou Aikaterini Kyritsi Anna Tagka Argyro Chatziioannou George K. Matsopoulos |
| author_facet | Ioannis A. Vezakis George I. Lambrou Aikaterini Kyritsi Anna Tagka Argyro Chatziioannou George K. Matsopoulos |
| author_sort | Ioannis A. Vezakis |
| collection | DOAJ |
| description | Epicutaneous patch testing is a well-established diagnostic method for identifying substances that may cause Allergic Contact Dermatitis (ACD), a common skin condition caused by exposure to environmental allergens. While the patch test remains the gold standard for identifying allergens, it is prone to observer bias and consumes valuable human resources. Deep learning models can be employed to address this challenge. In this study, we collected a dataset of 1579 multi-modal skin images from 200 patients using the Antera 3D<sup>®</sup> camera. We then investigated the feasibility of using a deep learning classifier for automating the identification of the allergens causing ACD. We propose a deep learning approach that utilizes a context-retaining pre-processing technique to improve the accuracy of the classifier. In addition, we find promise in the combination of the color image and false-color map of hemoglobin concentration to improve diagnostic accuracy. Our results showed that this approach can potentially achieve more than 86% recall and 94% specificity in identifying skin reactions, and contribute to faster and more accurate diagnosis while reducing clinician workload. |
| first_indexed | 2024-03-11T00:06:21Z |
| format | Article |
| id | doaj.art-485623cd7b8143008dcfcf4241ba74b0 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T00:06:21Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-485623cd7b8143008dcfcf4241ba74b02023-11-19T00:17:55ZengMDPI AGBioengineering2306-53542023-08-0110892410.3390/bioengineering10080924Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality PerformanceIoannis A. Vezakis0George I. Lambrou1Aikaterini Kyritsi2Anna Tagka3Argyro Chatziioannou4George K. Matsopoulos5Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, GreeceBiomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, GreeceFirst Department of Dermatology and Venereology, “Andreas Syggros” Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi St., 11621 Athens, GreeceFirst Department of Dermatology and Venereology, “Andreas Syggros” Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi St., 11621 Athens, GreeceFirst Department of Dermatology and Venereology, “Andreas Syggros” Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi St., 11621 Athens, GreeceBiomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, GreeceEpicutaneous patch testing is a well-established diagnostic method for identifying substances that may cause Allergic Contact Dermatitis (ACD), a common skin condition caused by exposure to environmental allergens. While the patch test remains the gold standard for identifying allergens, it is prone to observer bias and consumes valuable human resources. Deep learning models can be employed to address this challenge. In this study, we collected a dataset of 1579 multi-modal skin images from 200 patients using the Antera 3D<sup>®</sup> camera. We then investigated the feasibility of using a deep learning classifier for automating the identification of the allergens causing ACD. We propose a deep learning approach that utilizes a context-retaining pre-processing technique to improve the accuracy of the classifier. In addition, we find promise in the combination of the color image and false-color map of hemoglobin concentration to improve diagnostic accuracy. Our results showed that this approach can potentially achieve more than 86% recall and 94% specificity in identifying skin reactions, and contribute to faster and more accurate diagnosis while reducing clinician workload.https://www.mdpi.com/2306-5354/10/8/924patch testingcontact dermatitisallergic contact dermatitisdeep learningmachine learningimage analysis |
| spellingShingle | Ioannis A. Vezakis George I. Lambrou Aikaterini Kyritsi Anna Tagka Argyro Chatziioannou George K. Matsopoulos Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance Bioengineering patch testing contact dermatitis allergic contact dermatitis deep learning machine learning image analysis |
| title | Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance |
| title_full | Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance |
| title_fullStr | Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance |
| title_full_unstemmed | Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance |
| title_short | Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance |
| title_sort | detecting skin reactions in epicutaneous patch testing with deep learning an evaluation of pre processing and modality performance |
| topic | patch testing contact dermatitis allergic contact dermatitis deep learning machine learning image analysis |
| url | https://www.mdpi.com/2306-5354/10/8/924 |
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