  Sepsis is a leading cause of mortality and critical illness worldwide. While
robust biomarkers for early diagnosis are still missing, recent work indicates
that hyperspectral imaging (HSI) has the potential to overcome this bottleneck
by monitoring microcirculatory alterations. Automated machine learning-based
diagnosis of sepsis based on HSI data, however, has not been explored to date.
Given this gap in the literature, we leveraged an existing data set to (1)
investigate whether HSI-based automated diagnosis of sepsis is possible and (2)
put forth a list of possible confounders relevant for HSI-based tissue
classification. While we were able to classify sepsis with an accuracy of over
$98\,\%$ using the existing data, our research also revealed several subject-,
therapy- and imaging-related confounders that may lead to an overestimation of
algorithm performance when not balanced across the patient groups. We conclude
that further prospective studies, carefully designed with respect to these
confounders, are necessary to confirm the preliminary results obtained in this
study.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning