Fig. 1

Sublingual handheld vital microscopy (HVM) image sequences recorded in four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers were used for neuronal network training, model generation and internal validation, and two separate cohorts were used for external validation. Each dataset consisted of the neuronal network input matrix derived from the HVM image sequences, the functional microcirculatory variables derived from the image sequences by algorithm, and the reference COVID-19 disease state categorization. The training dataset was used to train the neuronal network, yielding the deep learning-based model (A), and to calculate the algorithm-based model (B). Both models were evaluated in the internal validation dataset in a bootstrapped process to identify the presence of microcirculatory alterations associated with COVID-19 disease state, and a combined model was calculated and internally validated in a separate, bootstrapped process (C). All models were then externally validated in an independent dataset (D). The results provided by all three models in the internal and external validation dataset were used to calculate the area under the receiver operating characteristic curve in a bootstrapped model, to quantify and compare their performance for identification of the presence of microcirculatory alterations associated with COVID-19 disease state in sublingual microcirculation HVM image sequences. HVM, handheld vital microscopy