The application of artificial intelligence (AI) techniques to the monitoring and treatment of
patients in the intensive care unit (ICU) is advancing rapidly from future possibility to …

The acute respiratory distress syndrome (ARDS) phenotype was first described over 50
years ago and since that time significant progress has been made in understanding the …

Clinical recognition of acute respiratory distress syndrome (ARDS) is delayed or missed
entirely in a substantial proportion of patients. In the LUNG SAFE study, the largest …

Background Acute hypoxemic respiratory failure (AHRF) and acute respiratory distress
syndrome (ARDS) are associated with high in-hospital mortality. However, in cohorts of …

Rationale: Lung ultrasound (LUS) is a promising tool for diagnosis of acute respiratory
distress syndrome (ARDS), but adequately sized studies with external validation are lacking …

There is a growing gap between studies describing the capabilities of artificial intelligence
(AI) diagnostic systems using deep learning versus efforts to investigate how or when to …

Background Acute respiratory distress syndrome (ARDS) is a common, but under-
recognised, critical illness syndrome associated with high mortality. An important factor in its …

Rationale: Patients with severe coronavirus disease (COVID-19) meet clinical criteria for the
acute respiratory distress syndrome (ARDS), yet early reports suggested they differ …

Background Current incidence and outcome of patients with acute hypoxaemic respiratory
failure requiring mechanical ventilation in the intensive care unit (ICU) are unknown …

We present an interpretable machine learning algorithm called 'eARDS'for predicting ARDS
in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the …