Vezakis Ioannis A, Lambrou George I, Kyritsi Aikaterini, Tagka Anna, Chatziioannou Argyro, Matsopoulos George K
Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece.
Choremeio Research Laboratory, First Department of Pediatrics, National and Kapodistrian University of Athens, 8 Thivon & Levadeias St., 11527 Athens, Greece.
Bioengineering (Basel). 2023 Aug 3;10(8):924. doi: 10.3390/bioengineering10080924.
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 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.
表皮贴片试验是一种成熟的诊断方法，用于识别可能导致过敏性接触性皮炎（ACD）的物质，这是一种因接触环境过敏原而引起的常见皮肤病。虽然贴片试验仍然是识别过敏原的金标准，但它容易受到观察者偏差的影响，并且消耗宝贵的人力资源。深度学习模型可用于应对这一挑战。在本研究中，我们使用Antera 3D相机从200名患者那里收集了一个包含1579张多模态皮肤图像的数据集。然后，我们研究了使用深度学习分类器自动识别导致ACD的过敏原的可行性。我们提出了一种深度学习方法，该方法利用一种保留上下文的预处理技术来提高分类器的准确性。此外，我们发现将彩色图像和血红蛋白浓度的伪彩色图相结合有望提高诊断准确性。我们的结果表明，这种方法在识别皮肤反应方面可能实现超过86%的召回率和94%的特异性，并有助于更快、更准确地诊断，同时减轻临床医生的工作量。
