dc.contributor.author
Schleusener, Johannes
dc.contributor.author
Guo, Shuxia
dc.contributor.author
Darvin, Maxim E.
dc.contributor.author
Thiede, Gisela
dc.contributor.author
Chernavskaia, Olga
dc.contributor.author
Knorr, Florian
dc.contributor.author
Lademann, Jürgen
dc.contributor.author
Popp, Jürgen
dc.contributor.author
Bocklitz, Thomas W.
dc.date.accessioned
2021-10-08T14:17:39Z
dc.date.available
2021-10-08T14:17:39Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/32246
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-31974
dc.description.abstract
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 tour 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 approximate to 65%. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Fiber-based SORS-SERDS system
en
dc.subject
machine learning algorithms
en
dc.subject
Raman spectra
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
413922
dcterms.bibliographicCitation.doi
10.1364/boe.413922
dcterms.bibliographicCitation.journaltitle
Biomedical Optics Express
dcterms.bibliographicCitation.number
2
dcterms.bibliographicCitation.originalpublishername
Optical Society of America (OSA)
dcterms.bibliographicCitation.pagestart
1123
dcterms.bibliographicCitation.pageend
1135
dcterms.bibliographicCitation.volume
12
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33680562
dcterms.isPartOf.eissn
2156-7085