Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

中文翻译：

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基于纤维的 SORS-SERDS 系统和化学计量学用于银屑病炎症性疾病体内诊断和治疗监测

牛皮癣被认为是一种广泛存在的皮肤病，可严重影响生活质量。目前，治疗一直持续到皮肤表面出现临床愈合为止。然而，看似正常的病变可能包含更深层次的改变。过早终止治疗会大大增加复发的风险。因此，需要更好地了解治疗过程的技术，特别是检测更深层次的病变变化。在这项研究中，我们开发了一种基于纤维的SORS-SERDS系统，结合机器学习算法，以无创地确定银屑病的治疗效率。该系统旨在从皮肤的三个不同深度获取拉曼光谱，从而提供有关深层皮肤变化的丰富信息。这样，有望在治疗时间过短的情况下防止复发。该方法通过 24 名患者两次就诊的研究得到验证：数据是在标准治疗开始时（就诊 1）和四个月后（就诊 2）获取的。在第 1 次就诊时，区分银屑病和正常皮肤的平均灵敏度达到 ≥85%。在第 2 次就诊时，患者根据临床表现得到治愈，平均灵敏度约为 65%。