CC BY 4.0Zerweck, LukasLukasZerweckWesarg, StefanStefanWesargKöhm, MichaelaMichaelaKöhmKohlhammer, JörnJörnKohlhammer2023-10-262023-10-262023https://publica.fraunhofer.de/handle/publica/452257https://doi.org/10.24406/publica-208010.1515/cdbme-2023-101710.24406/publica-2080In this work, we investigate the possibility of automatically and objectively evaluate an initialized therapy for psoriatic arthritis patients (therapy monitoring). Near infrared fluorescence optical imaging (NIR-FOI) is used to visualise the microcirculation in the hands, giving insights into possible inflammations (e.g. synovitis). Due to the time consuming and expansive data acquisition, we firstly investigate the feasibility of NIR-FOI in this scope of application using retrospective data. To increase the data quality and harmonize the heterogeneous patient population, certain inclusion criteria must be met, leaving only a limited amount of data sets for data analysis. A neural network was trained to segment the hands into areas of interest (e.g. joints and fingers) and an image registration pipeline was developed to compare identified regions of interest among different patient visits. It is shown that data from different visits are not directly comparable and thus, needs to be normalised. Due to the limited amount of meta data, only a simple normalisation can be performed. Even though with this normalisation the therapy monitoring cannot be carried out, the normalised data shows an increased agreement to a medical label in comparison to unnormalised data. Therefore, we conclude that further research can lead to a robust therapy monitoring algorithm.enBranche: HealthcareResearch Line: Computer vision (CV)Research Line: Machine learning (ML)LTA: Monitoring and control of processes and systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsMonitoringInfrared lightImage segmentationImage registrationjournal article