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  4. An objective, automated and robust scoring using fluorescence optical imaging to evaluate changes in micro-vascularisation indicating early arthritis
 
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September 2022
Journal Article
Title

An objective, automated and robust scoring using fluorescence optical imaging to evaluate changes in micro-vascularisation indicating early arthritis

Abstract
Fluorescence optical imaging technique (FOI) is a well-established and valid method for visualization of changes in micro vascularization at different organ systems. As increased vascularization is an early feature of joint inflammation, FOI is a promising method to assess arthritis of the hands. But usability of the method is limited to the assessors experience as the measurement of FOI is semi-quantitative using an individual grading system such as the fluorescence optical imaging activity score (FOIAS). The goal of the study was to automatically and thus, objectively analyze the measured fluorescence intensity generated by FOI to evaluate the amount of inflammation of each of the subject’s joints focusing on the distinction between normal joint status or arthritis in psoriatic arthritis patients compared to healthy volunteers. Due to the heterogeneity of the pathophysiological perfusion of the hands, a method to overcome the absoluteness of the data by extracting heatmaps out of the image stacks is developed. To calculate a heatmap for one patient, firstly the time series for each pixel is extracted, which is then represented by a feature value. Secondly, all feature values are clustered. The calculated cluster values represent the relativity between the different pixels and enable a comparison of multiple patients. As a metric to quantify the conspicuousness of a joint a score is calculated based on the extracted cluster values. These steps are repeated for a total number of three features. With this method a tendency towards a classification into unaffected and inflamed joints can be achieved. However, further research is necessary to transform the tendency into a robust classification model.
Author(s)
Zerweck, Lukas
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Köhm, Michaela
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Nguyen, Phuong-Ha
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Geisslinger, Gerd  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Behrens, Frank
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Pippow, Andreas  orcid-logo
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Journal
PLoS one. Online journal  
Open Access
DOI
10.1371/journal.pone.0274593
Additional link
Full text
Language
English
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
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