Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo

Title data

Schleusener, Johannes ; Guo, Shuxia ; Darvin, Maxim E. ; Thiede, Gisela ; Chernavskaia, Olga ; Knorr, Florian ; Lademann, Jürgen ; Popp, Jürgen ; Bocklitz, Thomas:
Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo.
In: Biomedical Optics Express. Vol. 12 (2021) Issue 2 . - pp. 1123-1135.
ISSN 2156-7085
DOI: https://doi.org/10.1364/BOE.413922

Official URL:

Abstract in another language

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 ≥q85% 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%.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Bend loss; Hollow core fibers; Laser scanning; Raman scattering; Raman spectroscopy; Tunable diode lasers
Institutions of the University:	Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT:	No
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 500 Science > 530 Physics
Date Deposited:	11 May 2023 12:59
Last Modified:	11 May 2023 12:59
URI:	https://eref.uni-bayreuth.de/id/eprint/76396