Review articles, commentaries, letters, editorials, and other informal publication types Published peer-reviewed scientific reports in English until the search date

Models predicting successful intubation or investigating obstructive sleep apnoea or noninvasive ventilationĬlinicians ‡ or previously validated models Primary use of neural signals or genetic/molecular markers as candidate predictors Investigation of outcomes directly related to mechanical ventilationĪpplication of AI to inform internal ventilator operation, increase clinical imaging techniques resolution, or identify clinical anatomy Non-human participants (animals or modelling data generated algorithmically)Īssess application of AI † to patient data Participants (adult and paediatric) undergoing invasive mechanical ventilation∗ AI, artificial intelligence NA, not applicable. ‡Human clinicians had to be equivalent level to board certification/completion of specialty training to be considered expert. †Artificial intelligence algorithms were defined as computational programmes with the capacity to learn, evaluate their own performance, and update their rules, to facilitate a prediction output. Paediatric patients were included to provide a comprehensive assessment of the literature. ∗Invasive mechanical ventilation is defined as a mechanism of respiratory support delivered to a patient with a tracheal tube. Table 1 Inclusion and exclusion criteria for study selection. Special Issue on Memory and Awareness in Anesthesia (PDF).Special Issue on Mass Casualty Medicine and Anaesthesia: Science and Clinical Practice (JPG).Special Issue on Thoracic Anaesthesia and Respiratory Physiology (PDF).Hong Kong College of Anaesthesiologists.College of Anaesthesiologists of Ireland.Memory, Awareness and Anaesthesia 2022 Special Collection.COVID-19 and the Anaesthetist: A Special Series.

Artificial academy 2 download hong