Machine Learning-assisted Analysis of Microcirculation Patterns and Parameters

Machine learning has been widely used in clinical medicine in recent years. It can be used for disease classification, disease severity grading, genetic testing, image analysis, adjuvant treatment recommendations, and predicting patient prognosis. Because sublingual microcirculation can be used for guiding shock resuscitation, a real time automated analysis is required for rapid changes of clinical condition. This study aims to use machine learning to analyze the parameters and patterns of sublingual microcirculation.