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Cell Imaging-Based Diagnostic Platform for Patients with Rheumatic Diseases
Background
Precision medicine based on cell-based assays has gradually gained popularity and is now essential for the treatment of patients suffering
from diseases with complex treatment regimens, such as rheumatoid arthritis.
Classifying patients according to their synovial fibroblast (SF) functional signature could lead to targeted therapies with a much higher
success rate than currently available disease-modifying drugs. To achieve this goal, we have successfully developed a series of assays that
enable functional screening of synovial fibroblasts and form the basis of a drug discovery approach for more effective personalized
treatment.
Aim
The aim of this work is to develop a model for automatically predicting the cellular stage from single-cell microscopy images, as such a
model would facilitate the personalization of treatments for patients suffering from rheumatic diseases. Therefore, the functional stage of
synovial fibroblasts (SF) - the cells of interest - should be classified into biologically meaningful classes based on physiological processes
such as mitochondrial activity, oxidative stress or apoptosis. Since some cells cannot be clearly assigned to a specific class, it may be
interesting to use not only supervised but also semi- or unsupervised approaches. All in all, the final goal is an easy-to-use pipeline for
single cell segmentation and classification that provides biologically meaningful outputs and visualizations.
Keywords: Deep Learning, Medical AI, Machine Learning, Computer Vision, Biomedical Image Analysis