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.
@article{arxiv.2106.08445,
title = {Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis},
author = {Maximilian Dietrich and Silvia Seidlitz and Nicholas Schreck and Manuel Wiesenfarth and Patrick Godau and Minu Tizabi and Jan Sellner and Sebastian Marx and Samuel Knödler and Michael M. Allers and Leonardo Ayala and Karsten Schmidt and Thorsten Brenner and Alexander Studier-Fischer and Felix Nickel and Beat P. Müller-Stich and Annette Kopp-Schneider and Markus A. Weigand and Lena Maier-Hein},
journal= {arXiv preprint arXiv:2106.08445},
year = {2021}
}
Comments
Maximilian Dietrich and Silvia Seidlitz contributed equally. Markus A. Weigand and Lena Maier-Hein contributed equally