Critical care : the official journal of the Critical Care Forum
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Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time and expertise. The aim of this study was to propose an externally validated deep learning model with uncertainty quantification and image segmentation for the automated assessment of ETT placement on ICU chest radiographs. ⋯ The CarinaNet model is an efficient and generalizable deep learning algorithm for the automated assessment of ETT placement on ICU chest radiographs. Uncertainty quantification can bring the attention of intensivists to chest radiographs that require an experienced human interpretation. Image segmentation provides intensivists with chest radiographs that are quickly interpretable and allows them to immediately assess the validity of model predictions. The CarinaNet model is ready to be evaluated in clinical studies.
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The global mortality rate of patients with MV is very high, despite a significant variation worldwide. Previous studies conducted in Sub-Saharan Africa among ICU patients focused on the pattern of admission and the incidence of mortality. However, the body of evidence on the clinical outcomes among patients with MV is still uncertain. ⋯ The higher 28-day mortality among ICU patients on mechanical ventilation in our study might be attributed to factors such as delayed patient presentation, lack of resources, insufficient healthcare infrastructure, lack of trained staff, and financial constraints.