Markus A. Weigand

Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel et al.

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcirculatory alterations, we propose a deep learning approach to automated sepsis diagnosis and mortality prediction using a single HSI cube acquired within seconds. In a prospective observational study, we collected HSI data from the palms and fingers of more than 480 intensive care unit patients. Neural networks applied to HSI measurements predicted sepsis and mortality with areas under the receiver operating characteristic curve (AUROCs) of 0.80 and 0.72, respectively. Performance improved substantially with additional clinical data, reaching AUROCs of 0.94 for sepsis and 0.83 for mortality. We conclude that deep learning-based HSI analysis enables rapid and noninvasive prediction of sepsis and mortality, with a potential clinical value for enhancing diagnosis and treatment.

Maximilian Dietrich, Silvia Seidlitz, Nicholas Schreck et al.

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.