Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence.

ABSTRACT:

Background

The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers.

Methods

Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers

SUBMITTER: Hilty MP 

PROVIDER: S-EPMC9568900 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature