Allergy Wheal and Erythema Segmentation Using Attention U-Net.

The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera. Subsequently, we assessed the deep learning model's performance by comparing the results with ground-truth data. Using contrast-limited adaptive histogram equalization (CLAHE), an image preprocessing technique designed to enhance image contrast, we augmented the chromatic contrast in 46 SPT images from 33 participants. We established a deep learning model for wheal and erythema segmentation using 144 and 150 training datasets, respectively. The wheal segmentation model achieved an accuracy of 0.9985, a sensitivity of 0.5621, a specificity of 0.9995, and a Dice similarity coefficient of 0.7079, whereas the erythema segmentation model achieved an accuracy of 0.9660, a sensitivity of 0.5787, a specificity of 0.97977, and a Dice similarity coefficient of 0.6636. The use of image preprocessing and deep learning technology in SPT is expected to have a significant positive impact on medical practice by ensuring the accurate segmentation of wheals and erythema, producing consistent evaluation results, and simplifying diagnostic processes.
Competing Interests: Declarations. Ethics Approval: This study was conducted in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Boards of Gachon University Gil Medical Center (GCIRB2022-264) and Ewha Womans University Medical Center (SEUMC 2022-12-049). Consent to Participate: Informed consent was obtained from all individual participants included in the study. Consent for Publication: Informed consent was obtained from all individual participants included in the study, and it included publication of the image of their skin on the back which did not contain their entire body or face in order not to interrupt their privacy. Competing Interests: The authors declare no competing interests.
(© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)